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: "characterizing honeypot captured cyber attacks statistical framework and case study"
  Abstract: Rigorously characterizing the statistical properties of cyber attacks is an important problem. In this paper, we propose the {\em first} statistical framework for rigorously analyzing honeypot-captured cyber attack data. The framework is built on the novel concept of {\em stochastic cyber attack process}, a new kind of mathematical objects for describing cyber attacks. To demonstrate use of the framework, we apply it to analyze a low-interaction honeypot dataset, while noting that the framework can be equally applied to analyze high-interaction honeypot data that contains richer information about the attacks. The case study finds, for the first time, that Long-Range Dependence (LRD) is exhibited by honeypot-captured cyber attacks. The case study confirms that by exploiting the statistical properties (LRD in this case), it is feasible to predict cyber attacks (at least in terms of attack rate) with good accuracy. This kind of prediction capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations. The idea of "gray-box" (rather than "black-box") prediction is central to the utility of the statistical framework, and represents a significant step towards ultimately understanding (the degree of) the {\em predictability} of cyber attacks.

Connected through hyperedge HE-1 (induced by source paper "metrics towards measuring cyber agility"):
  with members:
    - "a stochastic model of active cyber defense dynamics"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "optimizing active cyber defense"
    - "an extended stochastic model for quantitative security analysis of networked systems"
    - "predicting cyber attack rates with extreme values"
    - "cyber epidemic models with dependences"
    - "a new approach to modeling and analyzing security of networked systems"
    - "active cyber defense dynamics exhibiting rich phenomena"

Connected through hyperedge HE-2 (induced by source paper "a case study on using deep learning for network intrusion detection"):
  with members:
    - "metrics towards measuring cyber agility"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a novel hierarchical intrusion detection system based on decision tree and rules based models"
    - "entity embeddings of categorical variables"
    - "predicting cyber attack rates with extreme values"
    - "preventive and reactive cyber defense dynamics is globally stable"
    - "spatiotemporal patterns and predictability of cyberattacks"
    - "characterizing the power of moving target defense via cyber epidemic dynamics"

Connected through hyperedge HE-3 (induced by source paper "analyzing root causes of intrusion detection false negatives methodology and case study"):
  with members:
    - "metrics towards measuring cyber agility"
    - "benchmarking datasets for anomaly based network intrusion detection kdd cup 99 alternatives"
    - "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"
    - "predicting cyber attack rates with extreme values"
    - "an evasion and counter evasion study in malicious websites detection"

Connected through hyperedge HE-4 (induced by source paper "attacker behaviour profiling using stochastic ensemble of hidden markov models"):
  with members:
    - "a tutorial on principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "change point models for real time v2i cyber attack detection in a connected vehicle environment"):
  with members:
    - "survey on misbehavior detection in cooperative intelligent transportation systems"
    - "cyber physical systems security a survey"

Connected through hyperedge HE-6 (induced by source paper "exploratory data analysis of a network telescope traffic and prediction of port probing rates"):
  with members:
    - "predicting cyber attack rates with extreme values"

Connected through hyperedge HE-7 (induced by source paper "malware in the future forecasting analyst detection of cyber events"):
  with members:
    - "burstiness of intrusion detection process empirical evidence and a modeling approach"
    - "predicting cyber attack rates with extreme values"
    - "spatiotemporal patterns and predictability of cyberattacks"
    - "a widely applicable bayesian information criterion"

Connected through hyperedge HE-8 (induced by source paper "a survey on security metrics"):
  with members:
    - "a stochastic model of active cyber defense dynamics"
    - "a characterization of cybersecurity posture from network telescope data"
    - "an extended stochastic model for quantitative security analysis of networked systems"
    - "predicting cyber attack rates with extreme values"
    - "cyber epidemic models with dependences"
    - "a new approach to modeling and analyzing security of networked systems"
    - "active cyber defense dynamics exhibiting rich phenomena"
    - "characterizing the power of moving target defense via cyber epidemic dynamics"

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)