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: "near optimal optimistic reinforcement learning using empirical bernstein inequalities"
  Abstract: We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorithm, UCRL-V, achieves the optimal regret $\tilde{\mathcal{O}}(\sqrt{DSAT})$ up to logarithmic factors, and so our work closes a gap with the lower bound without additional assumptions on the MDP. We perform experiments in a variety of environments that validates the theoretical bounds as well as prove UCRL-V to be better than the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning near optimal policies with low inherent bellman error"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"

Connected through hyperedge HE-2 (induced by source paper "near optimal bayesian solution for unknown discrete markov decision process"):
  with members:
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "optimism in reinforcement learning and kullback leibler divergence"

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)