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: "optimal sampling based motion planning under differential constraints the driftless case"
  Abstract: Motion planning under differential constraints is a classic problem in robotics. To date, the state of the art is represented by sampling-based techniques, with the Rapidly-exploring Random Tree algorithm as a leading example. Yet, the problem is still open in many aspects, including guarantees on the quality of the obtained solution. In this paper we provide a thorough theoretical framework to assess optimality guarantees of sampling-based algorithms for planning under differential constraints. We exploit this framework to design and analyze two novel sampling-based algorithms that are guaranteed to converge, as the number of samples increases, to an optimal solution (namely, the Differential Probabilistic RoadMap algorithm and the Differential Fast Marching Tree algorithm). Our focus is on driftless control-affine dynamical models, which accurately model a large class of robotic systems. In this paper we use the notion of convergence in probability (as opposed to convergence almost surely): the extra mathematical flexibility of this approach yields convergence rate bounds - a first in the field of optimal sampling-based motion planning under differential constraints. Numerical experiments corroborating our theoretical results are presented and discussed.

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"
    - "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"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "sample complexity of probabilistic roadmaps via e nets"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "toward asymptotically optimal inspection planning via efficient near optimal graph search"
    - "robot motion planning in learned latent spaces"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "learning sampling distributions for robot motion planning"
    - "the critical radius in sampling based motion planning"
    - "data driven planning via imitation learning"

Connected through hyperedge HE-3 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning"):
  with members:
    - "lego leveraging experience in roadmap generation for sampling based planning"
    - "long range indoor navigation with prm rl"
    - "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "the laplacian in rl learning representations with efficient approximations"
    - "learning implicit sampling distributions for motion planning"
    - "learning sampling distributions for robot 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 "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"
    - "asymptotically optimal sampling based kinodynamic planning"

Connected through hyperedge HE-6 (induced by source paper "dispertio optimal sampling for safe deterministic sampling based motion planning"):
  with members:
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (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"
    - "analysis of asymptotically optimal sampling based motion planning algorithms for lipschitz continuous dynamical systems"

Connected through hyperedge HE-8 (induced by source paper "robust tracking with model mismatch for fast and safe planning an sos optimization approach"):
  with members:
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"
    - "funnel libraries for real time robust feedback motion planning"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "control design along trajectories with sums of squares programming"
    - "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)