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: "anomaly detection in online social networks"
  Abstract: Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research.

Connected through hyperedge HE-1 (induced by source paper "change detection in dynamic attributed networks"):
  with members:
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "link prediction in networks with nodes attributes by similarity propagation"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"

Connected through hyperedge HE-2 (induced by source paper "deep representation learning for social network analysis"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "techniques for interpretable machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attributed social network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "detecting problematic transactions in a consumer to consumer e commerce network"):
  with members:
    - "holoscope topology and spike aware fraud detection"
    - "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"
    - "an expert system for detecting automobile insurance fraud using social network analysis"

Connected through hyperedge HE-4 (induced by source paper "a survey on the security of pervasive online social networks posns"):
  with members:
    - "a framework for mitigating zero day attacks in iot"
    - "nhad neuro fuzzy based horizontal anomaly detection in online social networks"
    - "self enforcing game theory based resource allocation for lorawan assisted public safety communications"
    - "managing service heterogeneity using osmotic computing"
    - "netspam a network based spam detection framework for reviews in online social media"
    - "a privacy preserving community based p2p osns using broadcast encryption supporting recommendation mechanism"
    - "extension of mih to support fpmipv6 for optimized heterogeneous handover"
    - "analyzing resilience of interest based social networks against node and link failures"

Connected through hyperedge HE-5 (induced by source paper "sneak into devil s colony a study of fake profiles in online social networks and the cyber law"):
  with members:
    - "design and implementation of imacros based data crawler for behavioral analysis of facebook users"
    - "an army of me sockpuppets in online discussion communities"
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "sybilframe a defense in depth framework for structure based sybil detection"
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "online social networks threats and solutions"

Connected through hyperedge HE-6 (induced by source paper "nhad neuro fuzzy based horizontal anomaly detection in online social networks"):
  with members:
    - "towards detecting compromised accounts on social networks"

Connected through hyperedge HE-7 (induced by source paper "visual analytics for loan guarantee network risk management"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "technical report graph structured sparse optimization for connected subgraph detection"
    - "sales pipeline win propensity prediction a regression approach"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "general optimization technique for high quality community detection in complex networks"

Connected through hyperedge HE-8 (induced by source paper "user characterization for online social networks"):
  with members:
    - "the rise of social bots"
    - "friend or foe fake profile identification in online social 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.SI(Social and Information Networks)