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: "learning implicit sampling distributions for motion planning"
  Abstract: Sampling-based motion planners have experienced much success due to their ability to efficiently and evenly explore the state space. However, for many tasks, it may be more efficient to not uniformly explore the state space, especially when there is prior information about its structure. Previous methods have attempted to modify the sampling distribution using hand selected heuristics that can work well for specific environments but not universally. In this paper, a policy- search based method is presented as an adaptive way to learn implicit sampling distributions for different environments. It utilizes information from past searches in similar environments to generate better distributions in novel environments, thus reducing overall computational cost. Our method can be incor- porated with a variety of sampling-based planners to improve performance. Our approach is validated on a number of tasks, including a 7DOF robot arm, showing marked improvement in number of collision checks as well as number of nodes expanded compared with baseline methods.

Connected through hyperedge HE-1 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-2 (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"
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "the laplacian in rl learning representations with efficient approximations"
    - "learning sampling distributions for robot motion planning"
    - "robust locally linear controllable embedding"

Connected through hyperedge HE-3 (induced by source paper "learning to plan via neural exploration exploitation trees"):
  with members:
    - "learning to search with mctsnets"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep sequential models for sampling based planning"
    - "gated path planning networks"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning sampling distributions for robot motion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-4 (induced by source paper "lego leveraging experience in roadmap generation for sampling based planning"):
  with members:
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "deep sequential models for sampling based planning"
    - "deeply informed neural sampling for robot 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"

Connected through hyperedge HE-5 (induced by source paper "a novel approach to model exploration for value function learning"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "self normalizing neural networks"
    - "learning sampling distributions for robot motion planning"
    - "data driven planning via imitation learning"
    - "value iteration networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "adaptive path integral autoencoder representation learning and planning for dynamical systems"):
  with members:
    - "deepmind control suite"
    - "state representation learning for control an overview"
    - "inference suboptimality in variational autoencoders"
    - "approximate inference based motion planning by learning and exploiting low dimensional latent variable models"
    - "filtering variational objectives"
    - "learning sampling distributions for robot motion planning"
    - "particle smoothing for hidden diffusion processes adaptive path integral smoother"
    - "path integral networks end to end differentiable optimal control"

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)