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 "change detection in dynamic attributed networks"):
  Abstract: A network provides powerful means of representing complex relationships between entities by abstracting entities as vertices, and relationships as edges connecting vertices in a graph. Beyond the presence or absence of relationships, a network may contain additional information that can be attributed to the entities and their relationships. Attaching these additional attribute data to the corresponding vertices and edges yields an attributed graph. Moreover, in the majority of real-world applications, such as online social networks, financial networks and transactional networks, relationships between entities evolve over time. #R##N#Change detection in dynamic attributed networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and health care monitoring. It is a challenging problem because it involves a time sequence of attributed graphs, each of which is usually very large and can contain many attributes attached to the vertices and edges, resulting in a complex, high dimensional mathematical object. #R##N#In this survey we provide an overview of some of the existing change detection methods that utilize attribute information. We categorize these methods based on the levels of structure in the graph that are exploited to detect changes. These levels are vertices, edges, subgraphs, communities and the overall graph. We focus our attention on the strengths and weaknesses of these methods, including performance and scalability. Finally we discuss some publicly available dynamic network datasets and give a brief overview of simulation models to generate synthetic dynamic attributed networks.

Includes members:
  Member "anomaly detection in online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep representation learning for social network analysis")
  Member "interpreting communities based on the evolution of a dynamic attributed network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the vulnerability of communities in complex network an entropy approach")
  Member "link prediction in networks with nodes attributes by similarity propagation":
    also connected through other hyperedges:
      (none)
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "community detection in networks without observing edges")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "large scale spectral clustering using resistance distance and spielman teng solvers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "caddelag framework for distributed anomaly detection in large dense graph 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)