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: "feedback based tree search for reinforcement learning"
  Abstract: Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process. The terminal condition of the finite-horizon problems, or the leaf-node evaluator of the decision tree generated by MCTS, is specified using a combination of an estimated value function and an estimated policy function. The recommendations generated by the MCTS procedure are then provided as feedback in order to refine, through classification and regression, the leaf-node evaluator for the next iteration. We provide the first sample complexity bounds for a tree search-based RL algorithm. In addition, we show that a deep neural network implementation of the technique can create a competitive AI agent for the popular multi-player online battle arena (MOBA) game King of Glory.

Connected through hyperedge HE-1 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "go explore a new approach for hard exploration problems"
    - "continual lifelong learning with neural networks a review"
    - "rinascimento optimising statistical forward planning agents for playing splendor"
    - "a local approach to forward model learning results on the game of life game"
    - "deep reinforcement learning for general video game ai"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "open loop execution of tree search algorithms"
    - "machine theory of mind"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "mastering complex control in moba games with deep reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "an empirical model of large batch training"
    - "hierarchical macro strategy model for moba game ai"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "moba a new arena for game ai"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-4 (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"
    - "how to combine tree search methods in reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "how to combine tree search methods in 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-6 (induced by source paper "biasing mcts with features for general games"):
  with members:
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-7 (induced by source paper "how to combine tree search methods in reinforcement learning"):
  with members:
    - "multiple step greedy policies in online and approximate reinforcement learning"
    - "is q learning provably efficient"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "asynchronous methods for deep reinforcement learning"
    - "giraffe using deep reinforcement learning to play chess"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"

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.AI(Artificial Intelligence)