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: "information leakage games"
  Abstract: We consider a game-theoretic setting to model the interplay between attacker and defender in the context of information flow, and to reason about their optimal strategies. In contrast with standard game theory, in our games the utility of a mixed strategy is a convex function of the distribution on the defender's pure actions, rather than the expected value of their utilities. Nevertheless, the important properties of game theory, notably the existence of a Nash equilibrium, still hold for our (zero-sum) leakage games, and we provide algorithms to compute the corresponding optimal strategies. As typical in (simultaneous) game theory, the optimal strategy is usually mixed, i.e., probabilistic, for both the attacker and the defender. From the point of view of information flow, this was to be expected in the case of the defender, since it is well known that randomization at the level of the system design may help to reduce information leaks. Regarding the attacker, however, this seems the first work (w.r.t. the literature in information flow) proving formally that in certain cases the optimal attack strategy is necessarily probabilistic.

Connected through hyperedge HE-1 (induced by source paper "hybrid statistical estimation of mutual information and its application to information flow"):
  with members:
    - "what s the over under probabilistic bounds on information leakage"
    - "a game theoretic approach to information flow control via protocol composition"
    - "on the compositionality of quantitative information flow"
    - "quantitative information flow for scheduler dependent systems"
    - "speeding up smt based quantitative program analysis"
    - "a scalable approximate model counter"
    - "quantitative information flow as safety and liveness hyperproperties"

Connected through hyperedge HE-2 (induced by source paper "game theoretical modelling of network cyber security review paper"):
  with members:
    - "kidnapping model an extension of selten s game"
    - "optimizing active cyber defense"

Connected through hyperedge HE-3 (induced by source paper "leakage and protocol composition in a game theoretic perspective"):
  with members:
    - "on the compositionality of quantitative information flow"
    - "quantitative information flow under generic leakage functions and adaptive adversaries"
    - "type based self stabilisation for computational fields"
    - "security games with information leakage modeling and computation"
    - "calibrating data to sensitivity in private data analysis"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-4 (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"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "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)