human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "value function approximations for partially observable markov decision processes"):
  Abstract: Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.

Includes members:
  Member "stochastic simulation algorithms for dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factored probabilistic belief tracking")
  Member "a computational scheme for reasoning in dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning the structure of dynamic probabilistic networks")
  Member "solving pomdps by searching in policy space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  Member "approximate planning for factored pomdps using belief state simplification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse tree search optimality guarantees in pomdps with continuous observation spaces")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "region based approximations for planning in stochastic domains":
    also connected through other hyperedges:
      (none)
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")
  Member "planning with partially observable markov decision processes advances in exact solution method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "policy iteration for decentralized control of markov decision processes")

Question: Please tell me which class the source paper 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)