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: "predicting cyber attack rates with extreme values"
  Abstract: It is important to understand to what extent, and in what perspectives, cyber attacks can be predicted. Despite its evident importance, this problem was not investigated until very recently, when we proposed using the innovative methodology of {\em gray-box prediction}. This methodology advocates the use of gray-box models, which accommodate the statistical properties/phenomena exhibited by the data. Specifically, we showed that gray-box models that accommodate the Long-Range Dependence (LRD) phenomenon can predict the attack rate (i.e., the number of attacks per unit time) 1-hour ahead-of-time with an accuracy of 70.2-82.1\%. To the best of our knowledge, this is the first result showing the feasibility of prediction in this domain. We observe that the prediction errors are partly caused by the models' incapability in predicting the large attack rates, which are called {\em extreme values} in statistics. This motivates us to analyze the {\em extreme-value phenomenon}, by using two complementary approaches: the Extreme Value Theory (EVT) and the Time Series Theory (TST). In this paper, we show that EVT can offer long-term predictions (e.g., 24-hour ahead-of-time), while gray-box TST models can predict attack rates 1-hour ahead-of-time with an accuracy of 86.0-87.9\%. We explore connections between the two approaches, and point out future research directions. Although our prediction study is based on specific cyber attack data, our methodology can be equally applied to analyze any cyber attack data of its kind.

Connected through hyperedge HE-1 (induced by source paper "metrics towards measuring cyber agility"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "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"
    - "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"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "entity embeddings of categorical variables"
    - "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"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "an evasion and counter evasion study in malicious websites detection"

Connected through hyperedge HE-4 (induced by source paper "statistical models for the number of successful cyber intrusions"):
  with members:
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "exploratory data analysis of a network telescope traffic and prediction of port probing rates"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"

Connected through hyperedge HE-6 (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"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "spatiotemporal patterns and predictability of cyberattacks"
    - "a widely applicable bayesian information criterion"

Connected through hyperedge HE-7 (induced by source paper "a survey on security metrics"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "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"
    - "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)