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 "pushing the boundaries of asymptotic optimality for sampling based roadmaps in motion and task planning"):
  Abstract: Sampling-based roadmaps have been popular methods for robot motion and task planning, given their generality and effectiveness in high-dimensional configuration spaces (C-spaces). Following advances in random geometric graphs, a seminal analysis result argued the conditions for asymptotic optimality of these approaches. In particular, a connection radius for each new C-space sample needs to be in the order of $ \gamma (\log n / n)^{1/d} $, where $n$ is the existing number of roadmap nodes and $d$ is the dimensionality of the C-space. This prior analysis, as well as subsequent efforts, also specified a sufficient lower bound for the constant $\gamma$ for asymptotic optimality. All of these results assumed that for a finite number of samples there is a path with positive clearance from obstacles. Nevertheless, manipulation task planning requires solving problems were the start and the goal lie on the boundary of the configuration space. The current work builds on previous work, to: a) obtain an estimate of $\gamma$ in terms of a bound on the dispersion of the samples; and b) propose the modifications necessary to make asymptotic optimality hold when the start and goal lie on the boundary of the C-space under certain assumptions regarding the boundary. The last point generalizes these properties to manipulation task planning and reduces the method's requirements for a connection radius that achieves asymptotic optimality in this domain as well as the assumptions regarding the boundary's smoothness relative to prior work.

Includes members:
  Member "admissible abstractions for near optimal task and motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback")
  Member "fast high quality dual arm rearrangement in synchronous monotone tabletop setups":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on rearrangement of items stored in stacks")
  Member "ffrob leveraging symbolic planning for efficient task and motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "the critical radius in sampling based motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  Member "new perspective on sampling based motion planning via random geometric graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  Member "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptively informed trees ait fast asymptotically optimal path planning through adaptive heuristics")
  Member "sampling based algorithms for optimal motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")

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)