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 "clustering attributed graphs models measures and methods"):
  Abstract: Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

Includes members:
  Member "overlapping community detection in networks the state of the art and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "abacus frequent pattern mining based community discovery in multidimensional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "community detection in node attributed social networks a survey")
  Member "combinatorial analysis of multiple networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling")
  Member "an axiomatic study of objective functions for graph clustering":
    also connected through other hyperedges:
      (none)
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "efficient community detection in large networks using content and links":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "a classification for community discovery methods in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of community search over big graphs")
  Member "predicting links and inferring attributes using a social attribute network san":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring hierarchies in online social networks")

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)