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 detection in multiplex networks"):
  Abstract: A multiplex network models different modes of interaction among same-type entities. A great deal of attention has been devoted to the community detection problem in multiplex networks, that is, revealing meaningful patterns of node groupings into communities by considering different types of interactions among them. In this article, we provide the reader with a taxonomy on the community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive evaluation of the reviewed methods according to different criteria trying to answer three main questions, to what extent the evaluated methods are able to detect ground truth communities, to what extent different methods produce similar community structures and to what extent the evaluated methods are scalable. The ultimate goal of this survey is to drive scholars and practitioners in choosing the right method for the data and the task at hand.

Includes members:
  Member "overlapping community detection by local decentralised vertex centred process":
    also connected through other hyperedges:
      (none)
  Member "node centric community detection in multilayer networks with layer coverage diversification bias":
    also connected through other hyperedges:
      (none)
  Member "mapping higher order network flows in memory and multilayer networks with infomap":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing big data as networks new methods and insights")
  Member "clustering attributed graphs models measures and methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "community detection in node attributed social networks a survey")
  Member "community detection in multiplex networks using locally adaptive random walks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep node ranking structural network embedding and end to end node classification")
  Member "a local perspective on community structure in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "clustering on multi layer graphs via subspace analysis on grassmann manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning graphs from data a signal representation perspective")
  Member "mining attribute structure correlated patterns in large attributed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach")

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)