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: "benchmarking motion planning algorithms an extensible infrastructure for analysis and visualization"
  Abstract: Motion planning is a key problem in robotics that is concerned with finding a path that satisfies a goal specification subject to constraints. In its simplest form, the solution to this problem consists of finding a path connecting two states, and the only constraint is to avoid collisions. Even for this version of the motion planning problem, there is no efficient solution for the general case [1]. The addition of differential constraints on robot motion or more general goal specifications makes motion planning even harder. Given its complexity, most planning algorithms forego completeness and optimality for slightly weaker notions such as resolution completeness, probabilistic completeness [2], and asymptotic optimality.

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

Connected through hyperedge HE-2 (induced by source paper "a hierarchical network for diverse trajectory proposals"):
  with members:
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "edge slam edge points based monocular visual slam"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "end to end training of deep visuomotor policies"
    - "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-3 (induced by source paper "relevant region exploration on general cost maps for sampling based motion planning"):
  with members:
    - "informed sampling for asymptotically optimal path planning"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "using local experiences for global motion planning"):
  with members:
    - "learning to guide task and motion planning using score space representation"
    - "learning sampling distributions for robot motion planning"
    - "experience based planning with sparse roadmap spanners"

Connected through hyperedge HE-5 (induced by source paper "whole body control with self collision avoidance using vector field inequalities"):
  with members:
    - "active constraints using vector field inequalities for surgical robots"

Connected through hyperedge HE-6 (induced by source paper "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"):
  with members:
    - "carla an open urban driving simulator"
    - "deep reinforcement learning a brief survey"
    - "continuous control with deep reinforcement learning"
    - "simulated car racing championship competition software manual"

Connected through hyperedge HE-7 (induced by source paper "sampling based motion planning for active multirotor system identification"):
  with members:
    - "observability aware trajectory optimization for self calibration with application to uavs"
    - "linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles"
    - "trajectory synthesis for fisher information maximization"

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)