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: "tight regret bounds for model based reinforcement learning with greedy policies"
  Abstract: State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing \emph{full-planning} on Markov Decision Processes (MDPs) built by the gathered experience. In this paper, we focus on model-based RL in the finite-state finite-horizon MDP setting and establish that exploring with \emph{greedy policies} -- act by \emph{1-step planning} -- can achieve tight minimax performance in terms of regret, $\tilde{\mathcal{O}}(\sqrt{HSAT})$. Thus, full-planning in model-based RL can be avoided altogether without any performance degradation, and, by doing so, the computational complexity decreases by a factor of $S$. The results are based on a novel analysis of real-time dynamic programming, then extended to model-based RL. Specifically, we generalize existing algorithms that perform full-planning to such that act by 1-step planning. For these generalizations, we prove regret bounds with the same rate as their full-planning counterparts.

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"
    - "problem dependent reinforcement learning bounds which can identify bandit structure in mdps"

Connected through hyperedge HE-2 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-3 (induced by source paper "stabilizing deep reinforcement learning with conservative updates"):
  with members:
    - "distributional policy optimization an alternative approach for continuous control"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "on learning intrinsic rewards for policy gradient methods"
    - "addressing function approximation error in actor critic methods"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "stabilizing off policy reinforcement learning with conservative policy gradients"):
  with members:
    - "distributional policy optimization an alternative approach for continuous control"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "addressing function approximation error in actor critic methods"
    - "policy certificates towards accountable reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "safe policy improvement with baseline bootstrapping"
    - "deep reinforcement learning that matters"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"

Connected through hyperedge HE-5 (induced by source paper "multi step greedy and approximate real time dynamic programming"):
  with members:
    - "how to combine tree search methods in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "learning in real time search a unifying framework"

Connected through hyperedge HE-6 (induced by source paper "near optimal optimistic reinforcement learning using empirical bernstein inequalities"):
  with members:
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "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"
    - "optimism in reinforcement learning and kullback leibler divergence"

Connected through hyperedge HE-7 (induced by source paper "accelerating the computation of ucb and related indices for reinforcement learning"):
  with members:
    - "reinforcement learning a comparison of ucb versus alternative adaptive policies"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "tensorflow a system for large scale machine learning"
    - "hidden parameter markov decision processes a semiparametric regression approach for discovering latent task parametrizations"

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)