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 "hierarchical change point detection on dynamic networks"):
  Abstract: This paper studies change point detection on networks with community structures. It proposes a framework that can detect both local and global changes in networks efficiently. Importantly, it can clearly distinguish the two types of changes. The framework design is generic and as such several state-of-the-art change point detection algorithms can fit in this design. Experiments on both synthetic and real-world networks show that this framework can accurately detect changes while achieving up to 800X speedup.

Includes members:
  Member "fast change point detection on dynamic social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach")
  Member "graphprints towards a graph analytic method for network anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantifiable comparable evaluations of cyber defensive capabilities a survey novel unified approach")
  Member "anomaly detection in dynamic networks of varying size":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting topological changes in dynamic community networks")
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "change detection in dynamic attributed networks")
  Member "multi level anomaly detection on time varying graph data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantifiable comparable evaluations of cyber defensive capabilities a survey novel unified approach")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "a theoretical analysis of ndcg type ranking measures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "targeted display advertising the case of preferential attachment")
  Member "community structure and scale free collections of erdős renyi graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "artificial benchmark for community detection abcd fast random graph model with community structure")

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)