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 "community structure a comparative evaluation of community detection methods"):
  Abstract: Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimisation schemes as well as a comparison of their partioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.

Includes members:
  Member "an empirical characterization of community structures in complex networks using a bivariate map of quality metrics":
    also connected through other hyperedges:
      (none)
  Member "efficient method for estimating the number of communities in a network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension")
  Member "the many facets of community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedding based silhouette community detection")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "z score based modularity for community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detecting statistically significant communities")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "mixing local and global information for community detection in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sorec a social relation based centrality measure in mobile social networks")
  Member "towards linear time overlapping community detection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "community detection in node attributed social networks a survey")

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)