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 "new perspective on sampling based motion planning via random geometric graphs"):
  Abstract: Roadmaps constructed by many sampling-based motion planners coincide, in the absence of obstacles, with standard models of random geometric graphs (RGGs). Those models have been studied for several decades and by now a rich body of literature exists analyzing various properties and types of RGGs. In their seminal work on optimal motion planning Karaman and Frazzoli (2011) conjectured that a sampling-based planner has a certain property if the underlying RGG has this property as well. In this paper we settle this conjecture and leverage it for the development of a general framework for the analysis of sampling-based planners. Our framework, which we call localization-tessellation, allows for easy transfer of arguments on RGGs from the free unit-hypercube to spaces punctured by obstacles, which are geometrically and topologically much more complex. We demonstrate its power by providing alternative and (arguably) simple proofs for probabilistic completeness and asymptotic (near-)optimality of probabilistic roadmaps (PRMs). Furthermore, we introduce several variants of PRMs, analyze them using our framework, and discuss the implications of the analysis.

Includes members:
  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-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "on the relation between graph distance and euclidean distance in random geometric graphs":
    also connected through other hyperedges:
      (none)
  Member "on the hardness of unlabeled multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "asymptotically optimal motion planning using lower bounds on cost":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  Member "optimal sampling based motion planning under differential constraints the driftless case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "efficient high quality motion planning by fast all pairs r nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  Member "asymptotically near optimal rrt for fast high quality motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  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)