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: "an anytime algorithm for task and motion mdps"
  Abstract: Integrated task and motion planning has emerged as a challenging problem in sequential decision making, where a robot needs to compute high-level strategy and low-level motion plans for solving complex tasks. While high-level strategies require decision making over longer time-horizons and scales, their feasibility depends on low-level constraints based upon the geometries and continuous dynamics of the environment. The hybrid nature of this problem makes it difficult to scale; most existing approaches focus on deterministic, fully observable scenarios. We present a new approach where the high-level decision problem occurs in a stochastic setting and can be modeled as a Markov decision process. In contrast to prior efforts, we show that complete MDP policies, or contingent behaviors, can be computed effectively in an anytime fashion. Our algorithm continuously improves the quality of the solution and is guaranteed to be probabilistically complete. We evaluate the performance of our approach on a challenging, realistic test problem: autonomous aircraft inspection. Our results show that we can effectively compute consistent task and motion policies for the most likely execution-time outcomes using only a fraction of the computation required to develop the complete task and motion policy.

Connected through hyperedge HE-1 (induced by source paper "online replanning in belief space for partially observable task and motion problems"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "representing robot task plans as robust logical dynamical systems"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "sampling based methods for factored task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-2 (induced by source paper "hybrid planning for dynamic multimodal stochastic shortest paths"):
  with members:
    - "dynamic real time multimodal routing with hierarchical hybrid planning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "hierarchical solution of markov decision processes using macro actions"
    - "solving factored mdps with hybrid state and action variables"
    - "the ff planning system fast plan generation through heuristic search"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "anytime integrated task and motion policies for stochastic environments"):
  with members:
    - "sampling based methods for factored task and motion planning"
    - "active model learning and diverse action sampling for task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "deep reinforcement learning in parameterized action space"

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)