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 "stochastic 2 d motion planning with a pomdp framework"):
  Abstract: Motion planning is challenging when it comes to the case of imperfect state information. Decision should be made based on belief state which evolves according to the noise from the system dynamics and sensor measurement. In this paper, we propose the QV-Tree Search algorithm which combines the state-of-art offline and online approximation methods for POMDP. Instead of full node expansions in the tree search, only probable future observations are considered through forward sampling. This modification helps reduce online computation time and allows for GPU acceleration. We show using repre- sentative examples that the proposed QV-Tree Search is able to actively localize the robot in order to reach the goal location with high probability. The results of the proposed method is also compared with the A* and MDP algorithms, neither of which handles state uncertainty directly. The comparison shows that QV-Tree Search is able to drive the robot to the goal with higher success rate and fewer steps.

Includes members:
  Member "hyp despot a hybrid parallel algorithm for online planning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "point based pomdp algorithms improved analysis and implementation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")
  Member "heuristic search value iteration for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  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.RO(Robotics)