human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "simplified relative citation ratio for static paper ranking ufmg latin at wsdm cup 2016"
  Abstract: Static rankings of papers play a key role in the academic search setting. Many features are commonly used in the literature to produce such rankings, some examples are citation-based metrics, distinct applications of PageRank, among others. More recently, learning to rank techniques have been successfully applied to combine sets of features producing effective results. In this work, we propose the metric S-RCR, which is a simplified version of a metric called Relative Citation Ratio --- both based on the idea of a co-citation network. When compared to the classical version, our simplification S-RCR leads to improved efficiency with a reasonable effectiveness. We use S-RCR to rank over 120 million papers in the Microsoft Academic Graph dataset. By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016.

Connected through hyperedge HE-1 (induced by source paper "the coverage of microsoft academic analyzing the publication output of a university"):
  with members:
    - "leveraging citation networks to visualize scholarly influence over time"
    - "citation analysis with microsoft academic"
    - "semantometrics towards fulltext based research evaluation"
    - "quantifying the diaspora of knowledge in the last century"
    - "ranking academic institutions on potential paper acceptance in upcoming conferences"
    - "ensemble enabled weighted pagerank"
    - "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
    - "static ranking of scholarly papers using article level eigenfactor alef"

Connected through hyperedge HE-2 (induced by source paper "citation analysis with microsoft academic"):
  with members:
    - "quantifying the diaspora of knowledge in the last century"
    - "on the calculation of percentile based bibliometric indicators"
    - "the use of percentiles and percentile rank classes in the analysis of bibliometric data opportunities and limits"
    - "towards a new crown indicator some theoretical considerations"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IR(Information Retrieval)