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: "functional gradient motion planning in reproducing kernel hilbert spaces"
  Abstract: We introduce a functional gradient descent trajectory optimization algorithm for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs). Functional gradient algorithms are a popular choice for motion planning in complex many-degree-of-freedom robots, since they (in theory) work by directly optimizing within a space of continuous trajectories to avoid obstacles while maintaining geometric properties such as smoothness. However, in practice, functional gradient algorithms typically commit to a fixed, finite parameterization of trajectories, often as a list of waypoints. Such a parameterization can lose much of the benefit of reasoning in a continuous trajectory space: e.g., it can require taking an inconveniently small step size and large number of iterations to maintain smoothness. Our work generalizes functional gradient trajectory optimization by formulating it as minimization of a cost functional in an RKHS. This generalization lets us represent trajectories as linear combinations of kernel functions, without any need for waypoints. As a result, we are able to take larger steps and achieve a locally optimal trajectory in just a few iterations. Depending on the selection of kernel, we can directly optimize in spaces of trajectories that are inherently smooth in velocity, jerk, curvature, etc., and that have a low-dimensional, adaptively chosen parameterization. Our experiments illustrate the effectiveness of the planner for different kernels, including Gaussian RBFs, Laplacian RBFs, and B-splines, as compared to the standard discretized waypoint representation.

Connected through hyperedge HE-1 (induced by source paper "functional path optimisation for exploration in continuous occupancy maps"):
  with members:
    - "stochastic functional gradient for motion planning in continuous occupancy maps"
    - "occupancy map building through bayesian exploration"
    - "planning for robotic exploration based on forward simulation"

Connected through hyperedge HE-2 (induced by source paper "prediction of human full body movements with motion optimization and recurrent neural networks"):
  with members:
    - "vred a position velocity recurrent encoder decoder for human motion prediction"
    - "modeling human motion with quaternion based neural networks"
    - "learning human motion models for long term predictions"
    - "on human motion prediction using recurrent neural networks"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "newton methods for k order markov constrained motion problems"

Connected through hyperedge HE-3 (induced by source paper "continuous time gaussian process motion planning via probabilistic inference"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"
    - "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-4 (induced by source paper "motion planning in high dimensional spaces"):
  with members:
    - "manipulability maximization using continuous time gaussian processes"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"

Connected through hyperedge HE-5 (induced by source paper "directional grid maps modeling multimodal angular uncertainty in dynamic environments"):
  with members:
    - "unscented orientation estimation based on the bingham distribution"

Connected through hyperedge HE-6 (induced by source paper "occupancy map building through bayesian exploration"):
  with members:
    - "feedback motion planning under non gaussian uncertainty and non convex state constraints"
    - "planning for robotic exploration based on forward simulation"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "approximately optimal continuous time motion planning and control via probabilistic inference"):
  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)