human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a simple centrality index for scientific social recognition"):
  Abstract: We introduce a new centrality index for bipartite network of papers and authors that we call $K$-index. The $K$-index grows with the citation performance of the papers that cite a given researcher and can seen as a measure of scientific social recognition. Indeed, the $K$-index measures the number of hubs, defined in a self-consistent way in the bipartite network, that cites a given author. We show that the $K$-index can be computed by simple inspection of the Web of Science platform and presents several advantages over other centrality indexes, in particular Hirsch $h$-index. The $K$-index is robust to self-citations, is not limited by the total number of papers published by a researcher as occurs for the $h$-index and can distinguish in a consistent way researchers that have the same $h$-index but very different scientific social recognition. The $K$-index easily detects a known case of a researcher with inflated number of papers, citations and $h$-index due to scientific misconduct. Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the $K$-index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles or the $h$-index. Clustering researchers in a $K$ versus $h$ plot reveals interesting outliers that suggest that these two indexes can present complementary independent information.

Includes members:
  Member "are the authors of highly cited articles also the most productive ones":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ranking research institutions by the number of highly cited articles per scientist")
  Member "the h index can be easily manipulated":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "h index manipulation by undoing merges")
  Member "lobby index as a network centrality measure":
    also connected through other hyperedges:
      (none)
  Member "the inconsistency of the h index":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the h index for research assessment simple and popular but shown by mathematical analysis to be inconsistent and misleading")

Question: Please tell me which class the source paper 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.DL(Digital Libraries)