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 "fast planning over roadmaps via selective densification"):
  Abstract: We propose the Selective Densification method for fast motion planning through configuration space. We create a sequence of roadmaps by iteratively adding configurations. We organize these roadmaps into layers and add edges between identical configurations between layers. We find a path using best-first search, guided by our proposed estimate of remaining planning time. This estimate prefers to expand nodes closer to the goal and nodes on sparser layers. #R##N#We present proofs of the path quality and maximum depth of nodes expanded using our proposed graph and heuristic. We also present experiments comparing Selective Densification to bidirectional RRT-connect, as well as many graph search approaches. In difficult environments that require exploration on the dense layers we find Selective Densification finds solutions faster than all other approaches.

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 "the provable virtue of laziness in motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners")
  Member "anytime motion planning on large dense roadmaps with expensive edge evaluations":
    also connected through other hyperedges:
      (none)
  Member "densification strategies for anytime motion planning over large dense roadmaps":
    also connected through other hyperedges:
      (none)
  Member "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "deterministic sampling based motion planning optimality complexity and performance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning")
  Member "an asymptotically optimal sampling based algorithm for bi directional motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "experimental comparison of global motion planning algorithms for wheeled mobile robots")

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)