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 "despot online pomdp planning with regularization"):
  Abstract: The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online.

Includes members:
  Member "bandit algorithms for tree search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian optimization for backpropagation in monte carlo tree search")
  Member "pegasus a policy search method for large mdps and pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control")
  Member "point based pomdp algorithms improved analysis and implementation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "monte carlo bayesian reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments")
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  Member "perseus randomized point based value iteration for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "nonapproximability results for partially observable markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the cluster admission problem for cloud computing")

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)