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: "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
  Abstract: The Rapidly-exploring Random Tree (RRT) algorithm has been one of the most prevalent and popular motion-planning techniques for two decades now. Surprisingly, in spite of its centrality, there has been an active debate under which conditions RRT is probabilistically complete. We provide two new proofs of probabilistic completeness (PC) of RRT with a reduced set of assumptions. The first one for the purely geometric setting, where we only require that the solution path has a certain clearance from the obstacles. For the kinodynamic case with forward propagation of random controls and duration, we only consider in addition mild Lipschitz-continuity conditions. These proofs fill a gap in the study of RRT itself. They also lay sound foundations for a variety of more recent and alternative sampling-based methods, whose PC property relies on that of RRT.

Connected through hyperedge HE-1 (induced by source paper "refined analysis of asymptotically optimal kinodynamic planning in the state cost space"):
  with members:
    - "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"
    - "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 "scalable and probabilistically complete planning for robotic spatial extrusion"):
  with members:
    - "freeform assembly planning"
    - "automated sequence and motion planning for robotic spatial extrusion of 3d trusses"
    - "sampling based methods for factored task and motion planning"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "backward forward search for manipulation planning"
    - "sampling based algorithms for optimal motion planning"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning with probabilistically complete exploration"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "rl rrt kinodynamic motion planning via learning reachability estimators from rl policies"
    - "learning to plan via neural exploration exploitation trees"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "behavioral cloning from observation"
    - "parameter space noise for exploration"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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:
    - "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"
    - "optimal sampling based motion planning under differential constraints the driftless case"

Connected through hyperedge HE-6 (induced by source paper "revisiting the asymptotic optimality of rrt"):
  with members:
    - "the critical radius in sampling based motion planning"
    - "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-7 (induced by source paper "the critical radius in sampling based motion planning"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "scalable asymptotically optimal multi robot motion planning"
    - "minimizing task space frechet error via efficient incremental graph search"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "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"

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)