Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining Meta-indicators of University Ranking: A Machine Learning
  Approach Based on SHAP   University evaluation and ranking is an extremely complex activity. Major
universities are struggling because of increasingly complex indicator systems
of world university rankings. So can we find the meta-indicators of the index
system by simplifying the complexity? This research discovered three
meta-indicators based on interpretable machine learning. The first one is time,
to be friends with time, and believe in the power of time, and accumulate
historical deposits; the second one is space, to be friends with city, and grow
together by co-develop; the third one is relationships, to be friends with
alumni, and strive for more alumni donations without ceiling.

35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat