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 drift case with linear affine dynamics"
  Abstract: In this paper we provide a thorough, rigorous theoretical framework to assess optimality guarantees of sampling-based algorithms for drift control systems: systems that, loosely speaking, can not stop instantaneously due to momentum. We exploit this framework to design and analyze a sampling-based algorithm (the Differential Fast Marching Tree algorithm) that is asymptotically optimal, that is, it is guaranteed to converge, as the number of samples increases, to an optimal solution. In addition, our approach allows us to provide concrete bounds on the rate of this convergence. The focus of this paper is on mixed time/control energy cost functions and on linear affine dynamical systems, which encompass a range of models of interest to applications (e.g., double-integrators) and represent a necessary step to design, via successive linearization, sampling-based and provably-correct algorithms for non-linear drift control systems. Our analysis relies on an original perturbation analysis for two-point boundary value problems, which could be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning"):
  with members:
    - "scalable asymptotically optimal multi robot motion planning"
    - "effective metrics for multi robot motion planning"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "k color multi robot 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"
    - "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-3 (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-4 (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-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"
    - "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-6 (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-7 (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"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "control design along trajectories with sums of squares programming"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "safe motion planning in unknown environments optimality benchmarks and tractable policies"):
  with members:
    - "asymptotically optimal sampling based kinodynamic planning"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "julia a fast dynamic language for technical computing"
    - "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)