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 "experimental comparison of global motion planning algorithms for wheeled mobile robots"):
  Abstract: Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, a wide variety of motion planners, steer functions and path-improvement techniques have been proposed for such non-holonomic systems. With the objective of comparing this large assortment of state-of-the-art motion-planning techniques, we introduce a novel open-source motion-planning benchmark for wheeled mobile robots, whose scenarios resemble real-world applications (such as navigating warehouses, moving in cluttered cities or parking), and propose metrics for planning efficiency and path quality. Our benchmark is easy to use and extend, and thus allows practitioners and researchers to evaluate new motion-planning algorithms, scenarios and metrics easily. We use our benchmark to highlight the strengths and weaknesses of several common state-of-the-art motion planners and provide recommendations on when they should be used.

Includes members:
  Member "informed sampling for asymptotically optimal path planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  Member "a survey of motion planning and control techniques for self driving urban vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchical network for diverse trajectory proposals")
  Member "an asymptotically optimal sampling based algorithm for bi directional motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast planning over roadmaps via selective densification")
  Member "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "asymptotically optimal sampling based kinodynamic planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)