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: "open loop execution of tree search algorithms"
  Abstract: In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.

Connected through hyperedge HE-1 (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"
    - "machine theory of mind"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "feedback based tree search for 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-2 (induced by source paper "memory bounded open loop planning in large pomdps using thompson sampling"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "stacked thompson bandits"
    - "further optimal regret bounds for thompson sampling"
    - "heuristic search value iteration for pomdps"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-3 (induced by source paper "adaptive thompson sampling stacks for memory bounded open loop planning"):
  with members:
    - "memory bounded open loop planning in large pomdps using thompson sampling"
    - "stacked thompson bandits"
    - "further optimal regret bounds for thompson sampling"
    - "heuristic search value iteration for pomdps"

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)