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: "graphprints towards a graph analytic method for network anomaly detection"
  Abstract: This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.

Connected through hyperedge HE-1 (induced by source paper "quantifiable comparable evaluations of cyber defensive capabilities a survey novel unified approach"):
  with members:
    - "how do information security workers use host data a summary of interviews with security analysts"
    - "multi level anomaly detection on time varying graph data"
    - "coordination in network security games a monotone comparative statics approach"

Connected through hyperedge HE-2 (induced by source paper "efficient attack correlation and identification of attack scenarios based on network motifs"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"

Connected through hyperedge HE-3 (induced by source paper "a survey of intrusion detection systems leveraging host data"):
  with members:
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "malware detection on general purpose computers using power consumption monitoring a proof of concept and case study"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "identifying ecus using inimitable characteristics of signals in controller area networks"

Connected through hyperedge HE-4 (induced by source paper "towards malware detection via cpu power consumption data collection design and analytics extended version"):
  with members:
    - "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
    - "malware detection on general purpose computers using power consumption monitoring a proof of concept and case study"

Connected through hyperedge HE-5 (induced by source paper "stream graphs and link streams for the modeling of interactions over time"):
  with members:
    - "degree based outlier detection within ip traffic modelled as a link stream"
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "movie rating prediction using content based and link stream features"
    - "motifs in temporal networks"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "time series analysis of temporal networks"
    - "semirings for temporal network analysis"
    - "on multiaspect graphs"

Connected through hyperedge HE-6 (induced by source paper "hierarchical change point detection on dynamic networks"):
  with members:
    - "fast change point detection on dynamic social networks"
    - "anomaly detection in dynamic networks of varying size"
    - "detecting change points in the large scale structure of evolving networks"
    - "multi level anomaly detection on time varying graph data"
    - "graph based anomaly detection and description a survey"
    - "a theoretical analysis of ndcg type ranking measures"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-7 (induced by source paper "investigating the agility bias in dns graph mining"):
  with members:
    - "graph based anomaly detection and description a survey"

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)