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: "approximately optimal continuous time motion planning and control via probabilistic inference"
  Abstract: The problem of optimal motion planing and control is fundamental in robotics. However, this problem is intractable for continuous-time stochastic systems in general and the solution is difficult to approximate if non-instantaneous nonlinear performance indices are present. In this work, we provide an efficient algorithm, PIPC (Probabilistic Inference for Planning and Control), that yields approximately optimal policies with arbitrary higher-order nonlinear performance indices. Using probabilistic inference and a Gaussian process representation of trajectories, PIPC exploits the underlying sparsity of the problem such that its complexity scales linearly in the number of nonlinear factors. We demonstrate the capabilities of our algorithm in a receding horizon setting with multiple systems in simulation.

Connected through hyperedge HE-1 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  with members:
    - "stable concurrent controller composition for multi objective robotic tasks"
    - "representing robot task plans as robust logical dynamical systems"
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "learning latent space dynamics for tactile servoing"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-2 (induced by source paper "steap simultaneous trajectory estimation and planning"):
  with members:
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"

Connected through hyperedge HE-3 (induced by source paper "rmpflow a computational graph for automatic motion policy generation"):
  with members:
    - "riemannian motion policies"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "real time perception meets reactive motion generation"
    - "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 "continuous time gaussian process motion planning via probabilistic inference"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "riemannian motion policies"):
  with members:
    - "newton methods for k order markov constrained motion problems"

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)