human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems"
  Abstract: Over the last 20 years significant effort has been dedicated to the development of sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRT) and its asymptotically optimal version (e.g. RRT*). However, asymptotic optimality for RRT* only holds for linear and fully actuated systems or for a small number of non-linear systems (e.g. Dubin's car) for which a steering function is available. The purpose of this paper is to show that asymptotically optimal motion planning for dynamical systems with differential constraints can be achieved without the use of a steering function. We develop a novel analysis on sampling-based planning algorithms that sample the control space. This analysis demonstrated that asymptotically optimal path planning for any Lipschitz continuous dynamical system can be achieved by sampling the control space directly. We also determine theoretical bounds on the convergence rates for this class of algorithms. As the number of iterations increases, the trajectory generated by these algorithms, approaches the optimal control trajectory, with probability one. Simulation results are promising.

Connected through hyperedge HE-1 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "sampling based bottleneck pathfinding with applications to frechet matching"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "rrt2 0 for fast and optimal kinodynamic sampling based motion planning"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "completeness of randomized kinodynamic planners with state based steering"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "optimal sampling based motion planning under differential constraints the driftless case"

Connected through hyperedge HE-3 (induced by source paper "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"):
  with members:
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "informed sampling for asymptotically optimal path planning"
    - "the critical radius in sampling based motion planning"
    - "efficient sampling based bottleneck pathfinding over cost maps"
    - "completeness of randomized kinodynamic planners with state based steering"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"

Connected through hyperedge HE-4 (induced by source paper "completeness of randomized kinodynamic planners with state based steering"):
  with members:
    - "admissible velocity propagation beyond quasi static path planning for high dimensional robots"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "asymptotically optimal sampling based kinodynamic planning"):
  with members:
    - "an efficiently solvable quadratic program for stabilizing dynamic locomotion"
    - "fast marching tree a fast marching sampling based method for optimal motion planning in many dimensions"
    - "sparsification of motion planning roadmaps by edge contraction"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

Question: Please tell me which class the center node 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)