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: "asymptotically optimal sampling based kinodynamic planning"
  Abstract: Sampling-based algorithms are viewed as practical solutions for high-dimensional motion planning. Recent progress has taken advantage of random geometric graph theory to show how asymptotic optimality can also be achieved with these methods. Achieving this desirable property for systems with dynamics requires solving a two-point boundary value problem (BVP) in the state space of the underlying dynamical system. It is difficult, however, if not impractical, to generate a BVP solver for a variety of important dynamical models of robots or physically simulated ones. Thus, an open challenge was whether it was even possible to achieve optimality guarantees when planning for systems without access to a BVP solver. This work resolves the above question and describes how to achieve asymptotic optimality for kinodynamic planning using incremental sampling-based planners by introducing a new rigorous framework. Two new methods, Stable Sparse-RRT (SST) and SST*, result from this analysis, which are asymptotically near-optimal and optimal, respectively. The techniques are shown to converge fast to high-quality paths, while they maintain only a sparse set of samples, which makes them computationally efficient. The good performance of the planners is confirmed by experimental results using dynamical systems benchmarks, as well as physically simulated robots.

Connected through hyperedge HE-1 (induced by source paper "experimental comparison of global motion planning algorithms for wheeled mobile robots"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "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"
    - "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (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"
    - "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "asymptotically optimal sampling based planners"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "anytime multi arm task and motion planning for pick and place of individual objects via handoffs"
    - "revisiting the asymptotic optimality of rrt"
    - "robot motion planning in learned latent spaces"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "the provable virtue of laziness in motion planning"

Connected through hyperedge HE-4 (induced by source paper "toward asymptotically optimal inspection planning via efficient near optimal graph search"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "efficient motion planning for problems lacking optimal substructure"
    - "the provable virtue of laziness in motion planning"
    - "online interactive user guidance for high dimensional constrained motion planning"
    - "effective footstep planning for humanoids using homotopy class guidance"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-5 (induced by source paper "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"):
  with members:
    - "long range indoor navigation with prm rl"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "rrt colearn towards kinodynamic planning without numerical trajectory optimization"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-6 (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"
    - "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal motion planning using lower bounds on cost"
    - "optimal sampling based motion planning under differential constraints the driftless case"

Connected through hyperedge HE-7 (induced by source paper "motion planning explorer visualizing local minima using a local minima tree"):
  with members:
    - "rapidly exploring quotient space trees motion planning using sequential simplifications"
    - "motion planning in irreducible path spaces"
    - "quotient space motion planning"
    - "path homotopy invariants and their application to optimal trajectory planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "towards learning efficient maneuver sets for kinodynamic motion planning"):
  with members:
    - "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)