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 dynamic games approach to proactive defense strategies against advanced persistent threats in cyber physical systems"
  Abstract: Advanced Persistent Threats (APTs) have recently emerged as a significant security challenge for Cyber-Physical Systems (CPSs) due to APTs' stealthy, dynamic and adaptive nature. The proactive dynamic defense provides a strategic and holistic security mechanism to increase costs of attacks and mitigate risks. This work proposes a dynamic game framework to model the long-term interaction between the stealthy attacker and the proactive defender. The stealthy and deceptive behaviors are captured by the multistage game of incomplete information, where each player has his own private information unknown to the other. Both players act strategically according to their beliefs which are formed by multistage observation and learning. The solution concept of Perfect Bayesian Nash Equilibrium (PBNE) provides a useful prediction of both players' policies because no players benefit from unilateral deviations from the equilibrium. We propose an iterative algorithm to compute the PBNE and use Tennessee Eastman process as a benchmark case study. Our numerical experiment corroborates the analytical results and provides further insights into the design of proactive defense-in-depth strategies.

Connected through hyperedge HE-1 (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"
    - "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"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"

Connected through hyperedge HE-2 (induced by source paper "adaptive honeypot engagement through reinforcement learning of semi markov decision processes"):
  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"
    - "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"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"

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.GT(Computer Science and Game Theory)