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 "graph based anomaly detection and description a survey"):
  Abstract: Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured {\em graph} data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. #R##N#This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we provide a comprehensive exploration of both data mining and machine learning algorithms for these {\em detection} tasks. we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly {\em attribution} and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.

Includes members:
  Member "detecting change points in the large scale structure of evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "change detection in dynamic attributed networks")
  Member "deltacon a principled massive graph similarity function":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating descriptors for large graphs")
  Member "discriminative probabilistic models for relational data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "role dynamics fast mining of large dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal network representation learning")
  Member "estimating the prevalence of deception in online review communities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans")
  Member "identifying the signs of fraudulent accounts using data mining techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "netsimile a scalable approach to size independent network similarity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")
  Member "measuring tie strength in implicit social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "link classification and tie strength ranking in online social networks with exogenous interaction 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)