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 sampling distributions for robot motion planning"
  Abstract: A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally, these samples are drawn either probabilistically or deterministically to uniformly cover the state space. Yet, the motion of many robotic systems is often restricted to "small" regions of the state space, due to e.g. differential constraints or collision-avoidance constraints. To accelerate the planning process, it is thus desirable to devise non-uniform sampling strategies that favor sampling in those regions where an optimal solution might lie. This paper proposes a methodology for non-uniform sampling, whereby a sampling distribution is learnt from demonstrations, and then used to bias sampling. The sampling distribution is computed through a conditional variational autoencoder, allowing sample generation from the latent space conditioned on the specific planning problem. This methodology is general, can be used in combination with any sampling-based planner, and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. Specifically, on several planning problems, the proposed methodology is shown to effectively learn representations for the relevant regions of the state space, resulting in an order of magnitude improvement in terms of success rate and convergence to the optimal cost.

Connected through hyperedge HE-1 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search"):
  with members:
    - "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "learning to predict ego vehicle poses for sampling based nonholonomic motion planning"
    - "decentralized cooperative planning for automated vehicles with continuous monte carlo tree search"
    - "decentralized cooperative planning for automated vehicles with hierarchical monte carlo tree search"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-2 (induced by source paper "learn and link learning critical regions for efficient planning"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "ordered landmarks in planning"

Connected through hyperedge HE-3 (induced by source paper "human like planning for reaching in cluttered environments"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "deepmind control suite"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "introducing a human like planner for reaching in cluttered environments"):
  with members:
    - "deepmind control suite"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"

Connected through hyperedge HE-5 (induced by source paper "bayesian local sampling based planning"):
  with members:
    - "balancing global exploration and local connectivity exploitation with rapidly exploring random disjointed trees"
    - "sampling based algorithms for optimal motion planning"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "design space of behaviour planning for autonomous driving"):
  with members:
    - "learning on road visual control for self driving vehicles with auxiliary tasks"
    - "world models"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "environment independent task specifications via gltl"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

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)