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: "sample based planning with volumes in configuration space"
  Abstract: A simple sample-based planning method is presented which approximates connected regions of free space with volumes in Configuration space instead of points. The algorithm produces very sparse trees compared to point-based planning approaches, yet it maintains probabilistic completeness guarantees. The planner is shown to improve performance on a variety of planning problems, by focusing sampling on more challenging regions of a planning problem, including collision boundary areas such as narrow passages.

Connected through hyperedge HE-1 (induced by source paper "learning implicit sampling distributions for motion planning"):
  with members:
    - "learning sampling distributions for robot motion planning"
    - "generalizing informed sampling for asymptotically optimal sampling based kinodynamic planning via markov chain monte carlo"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "adam a method for stochastic optimization"
    - "asymptotically optimal sampling based kinodynamic planning"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "in hand manipulation via motion cones"):
  with members:
    - "reactive planar manipulation with convex hybrid mpc"
    - "experimental validation of contact dynamics for in hand manipulation"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "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)