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 "sparse tree search optimality guarantees in pomdps with continuous observation spaces"):
  Abstract: Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown unprecedented effectiveness in domains with continuous observation spaces. However there has been no formal theoretical justification for this technique. This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS), will estimate Q-values accurately with high probability and can be made to perform arbitrarily near the optimal solution by increasing computational power.

Includes members:
  Member "policy optimization via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "taylor expansion policy optimization")
  Member "the value of inferring the internal state of traffic participants for autonomous freeway driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "online algorithms for pomdps with continuous state action and observation spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts")
  Member "approximate planning for factored pomdps using belief state simplification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient decision making and belief space planning using sparse approximations")
  Member "incremental pruning a simple fast exact method for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")

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.LG(Machine Learning)