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: "drifting reinforcement learning the blessing of more optimism in face of endogenous exogenous dynamics"
  Abstract: We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts, ie, both the reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. This setting captures the endogenous and exogenous dynamics, uncertainty, and partial feedback in sequential decision-making scenarios, and finds applications in vehicle remarketing and real-time bidding. We first develop the Sliding Window Upper-Confidence bound for Reinforcement Learning with Confidence Widening (SWUCRL2-CW) algorithm, and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the Bandit-over-Reinforcement Learning (BORL) algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound, but in a parameter-free manner, ie, without knowing the variation budgets. Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared to existing algorithms. #R##N#Notably, the interplay between endogenous and exogenous dynamics presents a unique challenge, absent in existing (stationary and non-stationary) stochastic online learning settings, when we apply the conventional Optimism in Face of Uncertainty principle to design algorithms with provably low dynamic regret for RL in drifting MDPs. We overcome the challenge by a novel confidence widening technique that incorporates additional optimism into our learning algorithms to ensure low dynamic regret bounds. To extend our theoretical findings, we apply our framework to inventory control problems, and demonstrate how one can alternatively leverage special structures on the state transition distributions to bypass the difficulty in exploring time-varying environments.

Connected through hyperedge HE-1 (induced by source paper "provable self play algorithms for competitive reinforcement learning"):
  with members:
    - "reward free exploration for reinforcement learning"
    - "solving discounted stochastic two player games with near optimal time and sample complexity"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "corruption robust exploration in episodic reinforcement learning"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "feature based q learning for two player stochastic games"

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"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"

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.LG(Machine Learning)