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 "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.

Includes members:
  Member "deep reinforcement learning in parameterized action space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling")
  Member "theoretical foundations for abstraction based probabilistic planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robustness analysis of bayesian networks with local convex sets of distributions")

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)