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: "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"
  Abstract: Cyber attacks on both databases and critical infrastructure have threatened public and private sectors. Meanwhile, ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2007-2017 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their incentive structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.

Connected through hyperedge HE-1 (induced by source paper "game theory for cyber deception a tutorial"):
  with members:
    - "modeling analysis and mitigation of dynamic botnet formation in wireless iot networks"
    - "game theoretic analysis of cyber deception evidence based strategies and dynamic risk mitigation"
    - "dynamic bayesian games for adversarial and defensive cyber deception"
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "game theory meets network security a tutorial"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "istrict an interdependent strategic trust mechanism for the cloud enabled internet of controlled things"
    - "a dynamic game analysis and design of infrastructure network protection and recovery 125"

Connected through hyperedge HE-2 (induced by source paper "a games in games approach to mosaic command and control design of dynamic network of networks for secure and resilient multi domain operations"):
  with members:
    - "optimal secure two layer iot network design"
    - "interdependent strategic security risk management with bounded rationality in the internet of things"
    - "dynamic bayesian games for adversarial and defensive cyber deception"
    - "a multi layer feedback system approach to resilient connectivity of remotely deployed mobile internet of things"
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "istrict an interdependent strategic trust mechanism for the cloud enabled internet of controlled things"
    - "distributed and optimal resilient planning of large scale interdependent critical infrastructures"

Connected through hyperedge HE-3 (induced by source paper "strategic learning for active adaptive and autonomous cyber defense"):
  with members:
    - "modeling analysis and mitigation of dynamic botnet formation in wireless iot networks"
    - "adaptive honeypot engagement through reinforcement learning of semi markov decision processes"
    - "a dynamic games approach to proactive defense strategies against advanced persistent threats in cyber physical systems"
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "optimal timing in dynamic and robust attacker engagement during advanced persistent threats"
    - "a dynamic game analysis and design of infrastructure network protection and recovery 125"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"

Connected through hyperedge HE-4 (induced by source paper "toward proactive adaptive defense a survey on moving target defense"):
  with members:
    - "modeling and analysis of integrated proactive defense mechanisms for internet of things"
    - "moving target defense for deep visual sensing against adversarial examples"
    - "defending via strategic ml selection"
    - "a survey of moving target defenses for network security"
    - "frvm flexible random virtual ip multiplexing in software defined networks"
    - "cost benefit analysis of moving target defense in power grids"
    - "markov game modeling of moving target defense for strategic detection of threats in cloud networks"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"

Connected through hyperedge HE-5 (induced by source paper "adaptive honeypot engagement through reinforcement learning of semi markov decision processes"):
  with members:
    - "a dynamic games approach to proactive defense strategies against advanced persistent threats in cyber physical systems"
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "distributed and optimal resilient planning of large scale interdependent critical infrastructures"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "proactive defense against physical denial of service attacks using poisson signaling games"
    - "optimal timing in dynamic and robust attacker engagement during advanced persistent threats"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"

Connected through hyperedge HE-6 (induced by source paper "deceptive reinforcement learning under adversarial manipulations on cost signals"):
  with members:
    - "game theoretic analysis of cyber deception evidence based strategies and dynamic risk mitigation"
    - "reinforcement learning with perturbed rewards"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "istrict an interdependent strategic trust mechanism for the cloud enabled internet of controlled things"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "optimal timing in dynamic and robust attacker engagement during advanced persistent threats"
    - "reinforcement learning with a corrupted reward channel"

Connected through hyperedge HE-7 (induced by source paper "game theoretic analysis of cyber deception evidence based strategies and dynamic risk mitigation"):
  with members:
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "a game theoretic foundation of deception knowledge acquisition and fundamental limits"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "proactive defense against physical denial of service attacks using poisson signaling games"
    - "a dynamic game analysis and design of infrastructure network protection and recovery 125"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "deception by design evidence based signaling games for network defense"

Connected through hyperedge HE-8 (induced by source paper "cyber insurance"):
  with members:
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "a dynamic game analysis and design of infrastructure network protection and recovery 125"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "deception by design evidence based signaling games for network defense"
    - "large scale complex it systems"
    - "a local mean field analysis of security investments in networks"

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)