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: "mcts based on simple regret"
  Abstract: UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games and Markov decision processes, is based on UCB, a sampling policy for the Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However, search differs from MAB in that in MCTS it is usually only the final "arm pull" (the actual move selection) that collects a reward, rather than all "arm pulls". Therefore, it makes more sense to minimize the simple regret, as opposed to the cumulative regret. We begin by introducing policies for multi-armed bandits with lower finite-time and asymptotic simple regret than UCB, using it to develop a two-stage scheme (SR+CR) for MCTS which outperforms UCT empirically. #R##N#Optimizing the sampling process is itself a metareasoning problem, a solution of which can use value of information (VOI) techniques. Although the theory of VOI for search exists, applying it to MCTS is non-trivial, as typical myopic assumptions fail. Lacking a complete working VOI theory for MCTS, we nevertheless propose a sampling scheme that is "aware" of VOI, achieving an algorithm that in empirical evaluation outperforms both UCT and the other proposed algorithms.

Connected through hyperedge HE-1 (induced by source paper "static and dynamic values of computation in mcts"):
  with members:
    - "metareasoning for planning under uncertainty"
    - "selecting computations theory and applications"
    - "bayesian inference in monte carlo tree search"

Connected through hyperedge HE-2 (induced by source paper "asymmetric move selection strategies in monte carlo tree search minimizing the simple regret at max nodes"):
  with members:
    - "better computer go player with neural network and long term prediction"
    - "monte carlo tree search with heuristic evaluations using implicit minimax backups"
    - "bandit algorithms for tree search"

Connected through hyperedge HE-3 (induced by source paper "selecting computations theory and applications"):
  with members:
    - "reasoning about beliefs and actions under computational resource constraints"
    - "active model selection"

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"
    - "simple regret optimization in online planning for markov decision processes"
    - "doing better than uct rational monte carlo sampling in trees"

Connected through hyperedge HE-5 (induced by source paper "simple regret optimization in online planning for markov decision processes"):
  with members:
    - "selecting computations theory and applications"
    - "bandit algorithms for tree search"
    - "monte carlo planning theoretically fast convergence meets practical efficiency"
    - "doing better than uct rational monte carlo sampling in trees"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

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)