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 stackelberg game perspective on the conflict between machine learning and data obfuscation"
  Abstract: Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. Social network analysis, cookie-based advertising, and government surveillance are all evidence of the use of data for commercial and national interests. Public pressure, however, is mounting for the protection of privacy. Frameworks such as differential privacy offer machine learning algorithms methods to guarantee limits to information disclosure, but they are seldom implemented. Recently, however, developers have made significant efforts to undermine tracking through obfuscation tools that hide user characteristics in a sea of noise. These services highlight an emerging clash between tracking and data obfuscation. In this paper, we conceptualize this conflict through a dynamic game between users and a machine learning algorithm that uses empirical risk minimization. First, a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts. We study the interaction between the users and the learner using a Stackelberg game. The utility functions quantify accuracy using expected loss and privacy in terms of the bounds of differential privacy. In equilibrium, we find selfish users tend to cause significant utility loss to trackers by perturbing heavily, in a phenomenon reminiscent of public good games. Trackers, however, can improve the balance by proactively perturbing the data themselves. While other work in this area has studied privacy markets and mechanism design for truthful reporting of user information, we take a different viewpoint by considering both user and learner perturbation.

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 "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 game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-3 (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 game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-4 (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 game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"
    - "deception by design evidence based signaling games for network defense"

Connected through hyperedge HE-5 (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 game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "game theory meets network security a tutorial"):
  with members:
    - "adaptive strategic cyber defense for advanced persistent threats in critical infrastructure networks"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "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"
    - "deception by design evidence based signaling games for network defense"
    - "flip the cloud cyber physical signaling games in the presence of advanced persistent threats"
    - "heterogeneous learning in zero sum stochastic games with incomplete information"

Connected through hyperedge HE-7 (induced by source paper "modeling and analysis of leaky deception using signaling games with evidence"):
  with members:
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "deception by design evidence based signaling games for network defense"
    - "flip the cloud cyber physical signaling games in the presence of advanced persistent threats"
    - "finding deceptive opinion spam by any stretch of the imagination"
    - "nosebreak attacking honeynets"

Connected through hyperedge HE-8 (induced by source paper "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"):
  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"
    - "quantitative models of imperfect deception in network security using signaling games with evidence"
    - "information leakage games"
    - "deception by design evidence based signaling games for network defense"
    - "privacy games optimal user centric data obfuscation"
    - "flip the cloud cyber physical signaling games in the presence of advanced persistent threats"
    - "a game theoretic study on non monetary incentives in data analytics projects with privacy implications"

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)