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: "deterministic mdps with adversarial rewards and bandit feedback"
  Abstract: We consider a Markov decision process with deterministic state transition dynamics, adversarially generated rewards that change arbitrarily from round to round, and a bandit feedback model in which the decision maker only observes the rewards it receives. In this setting, we present a novel and efficient online decision making algorithm named MarcoPolo. Under mild assumptions on the structure of the transition dynamics, we prove that MarcoPolo enjoys a regret of O(T^(3/4)sqrt(log(T))) against the best deterministic policy in hindsight. Specifically, our analysis does not rely on the stringent unichain assumption, which dominates much of the previous work on this topic.

Connected through hyperedge HE-1 (induced by source paper "parameter free learning for evolving markov decision processes the blessing of more optimism"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "variational regret bounds for reinforcement learning"
    - "learning in structured mdps with convex cost functions improved regret bounds for inventory management"
    - "large scale markov decision processes with changing rewards"
    - "a sliding window algorithm for markov decision processes with arbitrarily changing rewards and transitions"
    - "learning to optimize under non stationarity"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "hedging the drift learning to optimize under non stationarity"

Connected through hyperedge HE-2 (induced by source paper "learning adversarial mdps with bandit feedback and unknown transition"):
  with members:
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "online convex optimization in adversarial markov decision processes"
    - "corruption robust exploration in episodic reinforcement learning"
    - "reinforcement learning under drift"
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "drifting reinforcement learning the blessing of more optimism in face of endogenous exogenous dynamics"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "taxi based emergency medical system"):
  with members:
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-4 (induced by source paper "relax but stay in control from value to algorithms for online markov decision processes"):
  with members:
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"

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)