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: "the critical radius in sampling based motion planning"
  Abstract: We develop a new analysis of sampling-based motion planning with uniform sampling, which significantly improves upon the celebrated result of Karaman and Frazzoli (2011) and subsequent work. Particularly, we prove the existence of a critical radius value proportional to $\Theta(n^{-1/d})$ for $n$ samples and $d$ dimensions: Below this value the planner is guaranteed to fail (similarly shown by the aforementioned work, ibid.). More importantly, for larger radius values the planner is asymptotically (near-)optimal (AO). A practical implication of our work is that AO is achieved when each sample is connected to only $\Theta(1)$ neighbors. This is in stark contrast to previous work which requires $O(\log n)$ connections, that are induced by a radius proportional to $\left(\frac{\log n}{n}\right)^{1/d}$. Our analysis is not restricted to PRM and is applicable to a variety of :PRM-based" planners, such as RRG, FMT* and BTT. Continuum percolation theory plays an important role in our proofs.

Connected through hyperedge HE-1 (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"
    - "data driven planning via imitation learning"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-2 (induced by source paper "pushing the boundaries of asymptotic optimality for sampling based roadmaps in motion and task planning"):
  with members:
    - "admissible abstractions for near optimal task and motion planning"
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "revisiting the asymptotic optimality of rrt"):
  with members:
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "incremental sampling based algorithm for minimum violation motion planning"
    - "kinodynamic rrt optimal motion planning for systems with linear differential constraints"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"

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)