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 "permanence and community structure in complex networks"):
  Abstract: The goal of community detection algorithms is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily driven to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this paper, we argue that the belongingness of nodes in a community is not uniform. #R##N#The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We discuss how permanence can help us understand and utilize the structure and evolution of communities by demonstrating that it can be used to -- (i) measure the persistence of a vertex in a community, (ii) design strategies to strengthen the community structure, (iii) explore the core-periphery structure within a community, and (iv) select suitable initiators for message spreading. #R##N#We demonstrate that the process of maximizing permanence produces meaningful communities that concur with the ground-truth community structure of the networks more accurately than eight other popular community detection algorithms. Finally, we show that the communities obtained by this method are (i) less affected by the changes in vertex-ordering, and (ii) more resilient to resolution limit, degeneracy of solutions and asymptotic growth of values.

Includes members:
  Member "genperm a unified method for detecting non overlapping and overlapping communities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metrics for community analysis a survey")
  Member "a new metric for quality of network community structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new measure of modularity density for community detection")
  Member "leveraging disjoint communities for detecting overlapping community structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensemble based overlapping community detection using disjoint community structures")
  Member "community detection and graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weighted laplacian and its theoretical applications")
  Member "enhancing community detection using a network weighting strategy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an ensemble based on a bi objective evolutionary spectral algorithm for graph clustering")
  Member "discovering link communities in complex networks by exploiting link dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantitative function and algorithm for community detection in bipartite 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")
  Member "comparative evaluation of community detection algorithms a topological approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ensemble clustering for graphs comparisons and applications")

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)