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: "how to combine tree search methods in reinforcement learning"
  Abstract: Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with specific planning methods such as Monte Carlo Tree Search (e.g. in AlphaZero). Referring to the planning problem as tree search, a reasonable practice in these implementations is to back up the value only at the leaves while the information obtained at the root is not leveraged other than for updating the policy. Here, we question the potency of this approach. Namely, the latter procedure is non-contractive in general, and its convergence is not guaranteed. Our proposed enhancement is straightforward and simple: use the return from the optimal tree path to back up the values at the descendants of the root. This leads to a $\gamma^h$-contracting procedure, where $\gamma$ is the discount factor and $h$ is the tree depth. To establish our results, we first introduce a notion called \emph{multiple-step greedy consistency}. We then provide convergence rates for two algorithmic instantiations of the above enhancement in the presence of noise injected to both the tree search stage and value estimation stage.

Connected through hyperedge HE-1 (induced by source paper "tight regret bounds for model based reinforcement learning with greedy policies"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "learning latent dynamics for planning from pixels"
    - "integrating planning for task completion dialogue policy learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "real time tree search with pessimistic scenarios"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "continual match based training in pommerman technical report"
    - "backplay man muss immer umkehren"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "pommerman a multi agent playground"
    - "feedback based tree search for reinforcement learning"
    - "thinking fast and slow with deep learning and tree search"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-3 (induced by source paper "multi step greedy and approximate real time dynamic programming"):
  with members:
    - "tight regret bounds for model based reinforcement learning with greedy policies"
    - "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"

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)