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: "using local experiences for global motion planning"
  Abstract: Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information, sampling-based planners are forced to explore uniformly or heuristically, which can lead to degraded performance. One way to improve performance is to use prior knowledge of environments to adapt the sampling strategy to the problem at hand. In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database. We synthesize an efficient global sampler by retrieving local experiences relevant to the given situation. Our method transfers knowledge effectively between diverse environments that share local primitives and speeds up the performance dramatically. Our results show, in terms of solution time, an improvement of multiple orders of magnitude in two traditionally challenging high-dimensional problems compared to state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "learned critical probabilistic roadmaps for robotic motion planning"):
  with members:
    - "lego leveraging experience in roadmap generation for sampling based planning"
    - "long range indoor navigation with prm rl"
    - "identifying critical regions for motion planning using auto generated saliency labels with convolutional neural networks"
    - "robot motion planning in learned latent spaces"
    - "the laplacian in rl learning representations with efficient approximations"
    - "learning implicit sampling distributions for motion planning"
    - "learning sampling distributions for robot motion planning"
    - "robust locally linear controllable embedding"

Connected through hyperedge HE-2 (induced by source paper "lego leveraging experience in roadmap generation for sampling based planning"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "deep sequential models for sampling based planning"
    - "deeply informed neural sampling for robot motion planning"
    - "learning implicit sampling distributions for motion planning"
    - "approximate inference based motion planning by learning and exploiting low dimensional latent variable models"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "generating long term trajectories using deep hierarchical networks"
    - "learning sampling distributions for robot 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)