human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "on a formal model of safe and scalable self driving cars"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "a model based decision theoretic perspective on automated cyber response"):
  with members:
    - "efficient planning under uncertainty with macro actions"
    - "online planning algorithms for pomdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch"):
  with members:
    - "bayesian reinforcement learning in factored pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "bayesian reinforcement learning a survey"
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "learning to make predictions in partially observable environments without a generative model"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-4 (induced by source paper "lets drive driving in a crowd by learning from tree search"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "gated path planning networks"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "socially aware motion planning with deep reinforcement learning"
    - "value iteration networks"

Connected through hyperedge HE-5 (induced by source paper "poddp partially observable differential dynamic programming for latent belief space planning"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "deep variational reinforcement learning for pomdps"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "pomdp lite for robust robot planning under uncertainty"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"

Connected through hyperedge HE-6 (induced by source paper "learning to plan hierarchically from curriculum"):
  with members:
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "action categorization for computationally improved task learning and planning"
    - "independently controllable features"
    - "automatic goal generation for reinforcement learning agents"
    - "independently controllable factors"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers"):
  with members:
    - "bi directional value learning for risk aware planning under uncertainty"
    - "low level active visual navigation increasing robustness of vision based localization using potential fields"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "efficient planning under uncertainty with macro actions"
    - "sampling based algorithms for optimal motion planning"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-8 (induced by source paper "active goal recognition"):
  with members:
    - "the automated mapping of plans for plan recognition"
    - "a new model of plan recognition"
    - "online planning algorithms for pomdps"

Question: Please tell me which class the center node 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)