human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "metrics towards measuring cyber agility"):
  Abstract: In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker’s strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a defender’s strategy changes over time. These changes may or may not be in direct response to a change in the opponent’s strategy. In any case, it is important to have a set of quantitative metrics to characterize and understand the effectiveness of attackers’ and defenders’ evolutionary strategies, which reflect their  cyber agility . Despite its clear importance, few systematic metrics have been developed to quantify the cyber agility of attackers and defenders. In this paper, we propose the first metric framework for measuring cyber agility in terms of the effectiveness of the dynamic evolution of cyber attacks and defenses. The proposed framework is generic and applicable to transform any relevant, quantitative, and/or conventional static security metrics (e.g., false positives and false negatives) into dynamic metrics to capture dynamics of system behaviors. In order to validate the usefulness of the proposed framework, we conduct case studies on measuring the evolution of cyber attacks and defenses using two real-world datasets. We discuss the limitations of the current work and identify future research directions.

Includes members:
  Member "characterizing honeypot captured cyber attacks statistical framework and case study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a case study on using deep learning for network intrusion detection")
  Member "a stochastic model of active cyber defense dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "preventive and reactive cyber defense dynamics with ergodic time dependent parameters is globally attractive")
  Member "push and pull based epidemic spreading in networks thresholds and deeper insights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "node diversification in complex networks by decentralized colouring")
  Member "optimizing active cyber defense":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "game theoretical modelling of network cyber security review paper")
  Member "an extended stochastic model for quantitative security analysis of networked systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on security metrics")
  Member "predicting cyber attack rates with extreme values":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a case study on using deep learning for network intrusion detection")
  Member "cyber epidemic models with dependences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on ethereum systems security vulnerabilities attacks and defenses")
  Member "a new approach to modeling and analyzing security of networked systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "preventive and reactive cyber defense dynamics with ergodic time dependent parameters is globally attractive")

Question: Please tell me which class the source paper 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)