human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an expert system for detecting automobile insurance fraud using social network analysis"
  Abstract: The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, Iterative Assessment Algorithm (IAA), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.

Connected through hyperedge HE-1 (induced by source paper "detecting problematic transactions in a consumer to consumer e commerce network"):
  with members:
    - "holoscope topology and spike aware fraud detection"
    - "anomaly detection in online social networks"
    - "machine learning for e mail spam filtering review techniques and trends"
    - "detecting criminal organizations in mobile phone networks"
    - "graph based anomaly detection and description a survey"
    - "identifying the signs of fraudulent accounts using data mining techniques"

Connected through hyperedge HE-2 (induced by source paper "empirical effect of graph embeddings on fraud detection risk mitigation"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "unifying local and global change detection in dynamic networks"):
  with members:
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection in online social networks"):
  with members:
    - "identifying the signs of fraudulent accounts using data mining techniques"

Connected through hyperedge HE-5 (induced by source paper "quantifying the consistency of scientific databases"):
  with members:
    - "network based statistical comparison of citation topology of bibliographic databases"
    - "model of complex networks based on citation dynamics"
    - "quantifying long term scientific impact"
    - "characterizing interdisciplinarity of researchers and research topics using web search engines"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"

Question: Please tell me which class the center node 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.AI(Artificial Intelligence)