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: "bad communities with high modularity"
  Abstract: In this paper we discuss some problematic aspects of Newman's modularity function QN. Given a graph G, the modularity of G can be written as QN = Qf -Q0, where Qf is the intracluster edge fraction of G and Q0 is the expected intracluster edge fraction of the null model, i.e., a randomly connected graph with same expected degree distribution as G. It follows that the maximization of QN must accomodate two factors pulling in opposite directions: Qf favors a small number of clusters and Q0 favors many balanced (i.e., with approximately equal degrees) clusters. In certain cases the Q0 term can cause overestimation of the true cluster number; this is the opposite of the well-known under estimation effect caused by the "resolution limit" of modularity. We illustrate the overestimation effect by constructing families of graphs with a "natural" community structure which, however, does not maximize modularity. In fact, we prove that we can always find a graph G with a "natural clustering" V of G and another, balanced clustering U of G such that (i) the pair (G; U) has higher modularity than (G; V) and (ii) V and U are arbitrarily different.

Connected through hyperedge HE-1 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance"):
  with members:
    - "using synthetic networks for parameter tuning in community detection"
    - "size matters a comparative analysis of community detection algorithms"
    - "graph sketching based massive data clustering"
    - "on the evaluation potential of quality functions in community detection for different contexts"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "extension of modularity density for overlapping community structure"
    - "axioms for graph clustering quality functions"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "evaluation of community structures using kappa index and f score instead of normalized mutual information"):
  with members:
    - "a new metric for quality of network community structure"
    - "defining and evaluating network communities based on ground truth"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "on varying topology of complex networks and performance limitations of community detection algorithms"):
  with members:
    - "tunable and growing network generation model with community structures"
    - "empirical comparison of algorithms for network community detection"
    - "a comparison of two techniques for bibliometric mapping multidimensional scaling and vos"
    - "finding community structure in mega scale social networks"

Connected through hyperedge HE-4 (induced by source paper "considerations about multistep community detection"):
  with members:
    - "clustering and community detection in directed networks a survey"
    - "graph degree linkage agglomerative clustering on a directed graph"

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.SI(Social and Information Networks)