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: "selecting computations theory and applications"
  Abstract: Sequential decision problems are often approximately solvable by simulating possible future action sequences. Metalevel decision procedures have been developed for selecting which action sequences to simulate, based on estimating the expected improvement in decision quality that would result from any particular simulation; an example is the recent work on using bandit algorithms to control Monte Carlo tree search in the game of Go. In this paper we develop a theoretical basis for metalevel decisions in the statistical framework of Bayesian selection problems, arguing (as others have done) that this is more appropriate than the bandit framework. We derive a number of basic results applicable to Monte Carlo selection problems, including the first finite sampling bounds for optimal policies in certain cases; we also provide a simple counterexample to the intuitive conjecture that an optimal policy will necessarily reach a decision in all cases. We then derive heuristic approximations in both Bayesian and distribution-free settings and demonstrate their superiority to bandit-based heuristics in one-shot decision problems and in Go.

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

Connected through hyperedge HE-2 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "modular networks learning to decompose neural computation"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-3 (induced by source paper "learning to select computations"):
  with members:
    - "learning to reinforcement learn"
    - "building machines that learn and think like people"
    - "metareasoning for planning under uncertainty"
    - "provably bounded optimal agents"

Connected through hyperedge HE-4 (induced by source paper "path finding under uncertainty through probabilistic inference"):
  with members:
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "a new algorithm for finding map assignments to belief networks"
    - "church a language for generative models"
    - "complexity of canadian traveler problem variants"
    - "the fast downward planning system"

Connected through hyperedge HE-5 (induced by source paper "metareasoning for planning under uncertainty"):
  with members:
    - "reasoning metareasoning and mathematical truth studies of theorem proving under limited resources"
    - "a bayesian approach to tackling hard computational problems"
    - "ideal reformulation of belief networks"
    - "reasoning about beliefs and actions under computational resource constraints"

Connected through hyperedge HE-6 (induced by source paper "maximum a posteriori estimation by search in probabilistic programs"):
  with members:
    - "church a language for generative models"
    - "anytime marginal map inference"
    - "solving map exactly using systematic search"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (induced by source paper "monte carlo planning theoretically fast convergence meets practical efficiency"):
  with members:
    - "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"
    - "mcts based on simple regret"
    - "doing better than uct rational monte carlo sampling in trees"

Connected through hyperedge HE-8 (induced by source paper "simple regret optimization in online planning for markov decision processes"):
  with members:
    - "bandit algorithms for tree search"
    - "monte carlo planning theoretically fast convergence meets practical efficiency"
    - "mcts based on simple regret"
    - "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)