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: "continuous time gaussian process motion planning via probabilistic inference"
  Abstract: We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a smal...

Connected through hyperedge HE-1 (induced by source paper "multimodal trajectory optimization for motion planning"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "learning novel policies for tasks"
    - "an algorithmic perspective on imitation learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "divide and conquer reinforcement learning"
    - "kernelized movement primitives"

Connected through hyperedge HE-2 (induced by source paper "the differentiable cross entropy method"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "exploring model based planning with policy networks"
    - "the limited multi label projection layer"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "unsupervised state representation learning in atari"
    - "adaptive and safe bayesian optimization in high dimensions via one dimensional subspaces"
    - "learning latent plans from play"
    - "learning action representations for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "differentiable gaussian process motion planning"):
  with members:
    - "leveraging experience in lazy search"
    - "differentiable mpc for end to end planning and control"
    - "neural ordinary differential equations"
    - "optnet differentiable optimization as a layer in neural networks"
    - "learning heuristic search via imitation"
    - "prm rl long range robotic navigation tasks by combining reinforcement learning and sampling based planning"
    - "learning to learn by gradient descent by gradient descent"
    - "value iteration networks"

Connected through hyperedge HE-4 (induced by source paper "steap simultaneous trajectory estimation and planning"):
  with members:
    - "approximately optimal continuous time motion planning and control 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-5 (induced by source paper "manipulation trajectory optimization with online grasp synthesis and selection"):
  with members:
    - "a billion ways to grasp an evaluation of grasp sampling schemes on a dense physics based grasp data set"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "grasp planning for customized grippers by iterative surface fitting"
    - "grasp pose detection in point clouds"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "real time grasp detection using convolutional neural networks"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "stochastic optimization for trajectory planning with heteroscedastic gaussian processes"):
  with members:
    - "spatio temporal multisensor calibration based on gaussian processes moving object tracking"
    - "manipulability maximization using continuous time gaussian processes"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "exactly sparse gaussian variational inference with application to derivative free batch nonlinear state estimation"):
  with members:
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"

Connected through hyperedge HE-8 (induced by source paper "rmpflow a computational graph for automatic motion policy generation"):
  with members:
    - "riemannian motion policies"
    - "approximately optimal continuous time motion planning and control 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"

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)