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 "metrics for community analysis a survey"):
  Abstract: Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over last one decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned out to be even complicated due to the fact that communities emerge in the network in various forms - disjoint, overlapping, hierarchical etc. Various heuristics have been proposed depending upon the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. There arises a need for an organized and detailed survey of the metrics proposed with respect to community detection and evaluation. In this survey, we present a comprehensive and structured overview of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real-world networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

Includes members:
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "on the evaluation potential of quality functions in community detection for different contexts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "genperm a unified method for detecting non overlapping and overlapping communities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "permanence and community structure in complex networks")
  Member "z score based modularity for community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "community structure a comparative evaluation of community detection methods")
  Member "permanence and community structure in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correlation based community detection")
  Member "generalized measures for the evaluation of community detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "generalization of clustering agreements and distances for overlapping clusters and network communities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "modular networks for validating community detection algorithms")

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.SI(Social and Information Networks)