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 "rrt2 0 for fast and optimal kinodynamic sampling based motion planning"):
  Abstract: We present RRT2.0: a simple yet efficient tree-based planner for asymptotically-optimal motion planning with kinodynamic constraints. RRT2.0 uses forward propagation and does not rely on the availability of a two-point boundary-value solver. The latter is a limiting requirement for some kinodynamic planners that are asymptotically optimal. The proposed approach improves upon a technique by Hauser and Zhou (2016), who explore an augmented state space with an additional coordinate, which endows every point in space with its cost-to-come value. Importantly, our optimality proofs require a milder and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying those assumptions, any trajectory having a piecewise-constant control function and positive clearance from obstacles can be approximated arbitrarily well by a trajectory found by RRT2.0. We provide experimental results demonstrating on a couple of robot models with kinodynamic constraints that RRT2.0 outperforms the existing alternatives in practice.

Includes members:
  Member "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "completeness of randomized kinodynamic planners with state based steering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation")
  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 "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space")
  Member "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "batch informed trees bit informed asymptotically optimal anytime search")
  Member "asymptotically optimal sampling based kinodynamic planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)