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 characterization"):
  Abstract: This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry.

Includes members:
  Member "from community detection to community profiling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sentiment driven community profiling and detection on social media")
  Member "on the evaluation potential of quality functions in community detection for different contexts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance")
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "clustering attributed graphs models measures and methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "community detection in node attributed social networks a survey")
  Member "a community role approach to assess social capitalists visibility in the twitter network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extraction and analysis of fictional character networks a survey")
  Member "interpreting communities based on the evolution of a dynamic attributed network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "change detection in dynamic attributed networks")
  Member "ged the method for group evolution discovery in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comparative study of different approaches for tracking communities in evolving social networks")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding temporal patterns using algebraic fingerprints")

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)