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 "heuristic search value iteration for pomdps"):
  Abstract: We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some benchmark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.

Includes members:
  Member "integrating planning and execution in stochastic domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decision theoretic planning structural assumptions and computational leverage")
  Member "structured reachability analysis for markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reachability and differential based heuristics for solving markov decision processes")
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")
  Member "spudd stochastic planning using decision diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maximum causal entropy specification inference from demonstrations")
  Member "speeding up the convergence of value iteration in partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world")
  Member "value function approximations for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "point based methods for model checking in partially observable 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)