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: "optimism in reinforcement learning and kullback leibler divergence"
  Abstract: We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. In MDPs, optimism can be implemented by carrying out extended value iterations under a constraint of consistency with the estimated model transition probabilities. The UCRL2 algorithm by Auer, Jaksch and Ortner (2009), which follows this strategy, has recently been shown to guarantee near-optimal regret bounds. In this paper, we strongly argue in favor of using the Kullback-Leibler (KL) divergence for this purpose. By studying the linear maximization problem under KL constraints, we provide an efficient algorithm, termed KL-UCRL, for solving KL-optimistic extended value iteration. Using recent deviation bounds on the KL divergence, we prove that KL-UCRL provides the same guarantees as UCRL2 in terms of regret. However, numerical experiments on classical benchmarks show a significantly improved behavior, particularly when the MDP has reduced connectivity. To support this observation, we provide elements of comparison between the two algorithms based on geometric considerations.

Connected through hyperedge HE-1 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-2 (induced by source paper "near optimal bayesian solution for unknown discrete markov decision process"):
  with members:
    - "near optimal optimistic reinforcement learning using empirical bernstein inequalities"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "near optimal optimistic reinforcement learning using empirical bernstein inequalities"):
  with members:
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "tight regret bounds for model based reinforcement learning with greedy policies"
    - "regret minimization for reinforcement learning by evaluating the optimal bias function"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning a comparison of ucb versus alternative adaptive policies"):
  with members:
    - "deep reinforcement learning that matters"
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"
    - "regret bounds for restless markov bandits"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "bandit learning through biased maximum likelihood estimation"):
  with members:
    - "on the prior sensitivity of thompson sampling"
    - "learning to optimize via information directed sampling"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "near optimal online egalitarian learning in general sum repeated matrix games"):
  with members:
    - "online reinforcement learning in stochastic games"
    - "a polynomial time nash equilibrium algorithm for repeated stochastic games"

Connected through hyperedge HE-7 (induced by source paper "stochastic online shortest path routing the value of feedback"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "matroid bandits fast combinatorial optimization with learning"
    - "online shortest path routing the value of information"
    - "adaptive shortest path routing under unknown and stochastically varying link states"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "the on line shortest path problem under partial monitoring"

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)