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 "asymptotically optimal sampling based planners"):
  Abstract: An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity. This comprehensive article covers the theoretical characteristics of asymptotic optimality of motion planning algorithms, and traces its origins, analysis models, practical performance, extensions, and applications.

Includes members:
  Member "drrt scalable and informed asymptotically optimal multi robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  Member "anytime multi arm task and motion planning for pick and place of individual objects via handoffs":
    also connected through other hyperedges:
      (none)
  Member "revisiting the asymptotic optimality of rrt":
    also connected through other hyperedges:
      (none)
  Member "robot motion planning in learned latent spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "fast anytime motion planning for prehensile manipulation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample complexity of probabilistic roadmaps via e nets")
  Member "group marching tree sampling based approximately optimal motion planning on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gpu parallelization of policy iteration rrt")
  Member "the provable virtue of laziness in motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast planning over roadmaps via selective densification")

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)