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 "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks"):
  Abstract: Though much work has been done on ensemble clustering in data mining, the application of ensemble methods to community detection in networks is in its infancy. In this paper, we propose two ensemble methods: ENDISCO and MEDOC. ENDISCO performs disjoint community detection. In contrast, MEDOC performs disjoint, overlapping, and fuzzy community detection and represents the first ever ensemble method for fuzzy and overlapping community detection. We run extensive experiments with both algorithms against both synthetic and several real-world datasets for which community structures are known. We show that ENDISCO and MEDOC both beat the best-known existing standalone community detection algorithms (though we emphasize that they leverage them). In the case of disjoint community detection, we show that both ENDISCO and MEDOC beat an existing ensemble community detection algorithm both in terms of multiple accuracy measures and run-time. We further show that our ensemble algorithms can help explore core-periphery structure of network communities, identify stable communities in dynamic networks and help solve the "degeneracy of solutions" problem, generating robust results.

Includes members:
  Member "ensemble based algorithms to detect disjoint and overlapping communities in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensemble based overlapping community detection using disjoint community structures")
  Member "permanence and community structure in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation based community detection")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "personalized pagerank solution paths":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a short graph fourier transform via personalized pagerank vectors")
  Member "leveraging disjoint communities for detecting overlapping community structure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ensemble based overlapping community detection using disjoint community structures")
  Member "yasca a collective intelligence approach for community detection in complex networks":
    also connected through other hyperedges:
      (none)
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning with relational information for short sequences")

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)