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 of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations"
  Abstract: The effective deployment of unmanned aerial vehicle (UAV) systems and services hinges on securing them against cyber-physical attacks that can jeopardize their mission or use them to intrude into secured perimeters. In this paper, a novel mathematical framework is introduced for modeling and analyzing the cyber-physical security of time-critical UAV applications. A general UAV security network interdiction game is formulated to model interactions between a UAV operator and an interdictor, each of which can be benign or malicious. In this game, the interdictor chooses the optimal location(s) from which to target the drone system by interdicting the potential paths of the UAVs. Meanwhile, the UAV operator responds by finding an optimal path selection policy that enables its UAVs to evade attacks and minimize their mission completion time. New notions from cumulative prospect theory (PT) are incorporated into the game to capture the operator's and interdictor's subjective valuations of mission completion times and perceptions of the risk levels facing the UAVs. The equilibrium of the game, with and without PT, is then analytically characterized and studied. Novel algorithms are then proposed to reach the game's equilibria under both PT and classical game theory. Simulation results show the properties of the equilibrium for both the rational and PT cases. The results show that the operator's and interdictor's bounded rationality is more likely to be disadvantageous to the UAV operator. In particular, the more distorted the perceptions and valuations of the operator are, the higher is its achieved expected mission completion time.

Connected through hyperedge HE-1 (induced by source paper "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"):
  with members:
    - "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "machine learning for wireless connectivity and security of cellular connected uavs"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "single controller stochastic games for optimized moving target defense"

Connected through hyperedge HE-2 (induced by source paper "on greedy and strategic evaders in sequential interdiction settings with incomplete information"):
  with members:
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"
    - "optimal interdiction of unreactive markovian evaders"

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)