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: "deeply informed neural sampling for robot motion planning"
  Abstract: Sampling-based Motion Planners (SMPs) have become increasingly popular as they provide collision-free path solutions regardless of obstacle geometry in a given environment. However, their computational complexity increases significantly with the dimensionality of the motion planning problem. Adaptive sampling is one of the ways to speed up SMPs by sampling a particular region of a configuration space that is more likely to contain an optimal path solution. Although there are a wide variety of algorithms for adaptive sampling, they rely on hand-crafted heuristics; furthermore, their performance decreases significantly in high-dimensional spaces. In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP). DeepSMP generates samples for SMPs and enhances their overall speed significantly while exhibiting efficient scalability to higher-dimensional problems. DeepSMP's neural architecture comprises of a Contractive AutoEncoder which encodes given workspaces directly from a raw point cloud data, and a Dropout-based stochastic deep feedforward neural network which takes the workspace encoding, start and goal configuration, and iteratively generates feasible samples for SMPs to compute end-to-end collision-free optimal paths. DeepSMP is not only consistently computationally efficient in all tested environments but has also shown remarkable generalization to completely unseen environments. We evaluate DeepSMP on multiple planning problems including planning of a point-mass robot, rigid-body, 6-link robotic manipulator in various 2D and 3D environments. The results show that on average our method is at least 7 times faster in point-mass and rigid-body case and about 28 times faster in 6-link robot case than the existing state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "efficient exploration in constrained environments with goal oriented reference path"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "learning robotic assembly from cad"
    - "learning navigation behaviors end to end with autorl"
    - "state representation learning for control an overview"
    - "motion planning networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (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-3 (induced by source paper "harnessing reinforcement learning for neural motion planning"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "deep learning using rectified linear units relu"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "reinforced imitation sample efficient deep reinforcement learning for map less navigation by leveraging prior demonstrations"
    - "motion planning networks"

Connected through hyperedge HE-4 (induced by source paper "mapping state space using landmarks for universal goal reaching"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "composable planning with attributes"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "semi parametric topological memory for navigation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "motion planning networks"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "potentially guided bidirectionalized rrt for fast optimal path planning in cluttered environments"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "learning sampling distributions for robot motion planning"
    - "potential functions based sampling heuristic for optimal path planning"
    - "end to end learning for self driving cars"
    - "overcoming catastrophic forgetting in neural networks"
    - "value iteration networks"

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)