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: "simple regret optimization in online planning for markov decision processes"
  Abstract: We consider online planning in Markov decision processes (MDPs). In online planning, the agent focuses on its current state only, deliberates about the set of possible policies from that state onwards and, when interrupted, uses the outcome of that exploratory deliberation to choose what action to perform next. The performance of algorithms for online planning is assessed in terms of simple regret, which is the agent's expected performance loss when the chosen action, rather than an optimal one, is followed. #R##N#To date, state-of-the-art algorithms for online planning in general MDPs are either best effort, or guarantee only polynomial-rate reduction of simple regret over time. Here we introduce a new Monte-Carlo tree search algorithm, BRUE, that guarantees exponential-rate reduction of simple regret and error probability. This algorithm is based on a simple yet non-standard state-space sampling scheme, MCTS2e, in which different parts of each sample are dedicated to different exploratory objectives. Our empirical evaluation shows that BRUE not only provides superior performance guarantees, but is also very effective in practice and favorably compares to state-of-the-art. We then extend BRUE with a variant of "learning by forgetting." The resulting set of algorithms, BRUE(alpha), generalizes BRUE, improves the exponential factor in the upper bound on its reduction rate, and exhibits even more attractive empirical performance.

Connected through hyperedge HE-1 (induced by source paper "skinnerdb regret bounded query evaluation via reinforcement learning"):
  with members:
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "sampling based query re optimization"
    - "ordering selection operators using the minmax regret rule"
    - "bandit algorithms for tree search"
    - "robust estimation of resource consumption for sql queries using statistical techniques"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-2 (induced by source paper "active end effector pose selection for tactile object recognition through monte carlo tree search"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "recovering 6d object pose and predicting next best view in the crowd"
    - "efficient touch based localization through submodularity"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "active tactile object recognition by monte carlo tree search"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "monte carlo planning theoretically fast convergence meets practical efficiency"):
  with members:
    - "selecting computations theory and applications"
    - "bandit algorithms for tree search"
    - "pegasus a policy search method for large mdps and pomdps"
    - "mcts based on simple regret"
    - "doing better than uct rational monte carlo sampling in trees"

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)