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: "network traffic anomaly detection"
  Abstract: This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today's social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.

Connected through hyperedge HE-1 (induced by source paper "your smart home can t keep a secret towards automated fingerprinting of iot traffic with neural networks"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "peek a boo i see your smart home activities even encrypted"
    - "automated website fingerprinting through deep learning"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "iotscanner detecting and classifying privacy threats in iot neighborhoods"
    - "iot sentinel demo automated device type identification for security enforcement in iot"

Connected through hyperedge HE-2 (induced by source paper "degree based outlier detection within ip traffic modelled as a link stream"):
  with members:
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "an algebraic approach to temporal network analysis based on temporal quantities"
    - "a unifying model for representing time varying graphs"
    - "graph based anomaly detection and description a survey"
    - "temporal reachability graphs"
    - "netsimile a scalable approach to size independent network similarity"

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.CR(Cryptography and Security)