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 "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"):
  Abstract: The Rapidly-exploring Random Tree (RRT) algorithm has been one of the most prevalent and popular motion-planning techniques for two decades now. Surprisingly, in spite of its centrality, there has been an active debate under which conditions RRT is probabilistically complete. We provide two new proofs of probabilistic completeness (PC) of RRT with a reduced set of assumptions. The first one for the purely geometric setting, where we only require that the solution path has a certain clearance from the obstacles. For the kinodynamic case with forward propagation of random controls and duration, we only consider in addition mild Lipschitz-continuity conditions. These proofs fill a gap in the study of RRT itself. They also lay sound foundations for a variety of more recent and alternative sampling-based methods, whose PC property relies on that of RRT.

Includes members:
  Member "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "informed sampling for asymptotically optimal path planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  Member "the critical radius in sampling based motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  Member "efficient sampling based bottleneck pathfinding over cost maps":
    also connected through other hyperedges:
      (none)
  Member "completeness of randomized kinodynamic planners with state based steering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rrt2 0 for fast and optimal kinodynamic sampling based motion planning")
  Member "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  Member "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")

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)