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: "motion planning networks"
  Abstract: Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

Connected through hyperedge HE-2 (induced by source paper "probably approximately correct vision based planning using motion primitives"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "pytorch an imperative style high performance deep learning library"
    - "pac bayes with backprop"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "pac bayes control learning policies that provably generalize to novel environments"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "pac bayes control synthesizing controllers that provably generalize to novel environments"

Connected through hyperedge HE-3 (induced by source paper "sub goal trees a framework for goal based reinforcement learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "floyd warshall reinforcement learning learning from past experiences to reach new goals"
    - "hindsight experience replay"
    - "prediction and control with temporal segment models"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-4 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "leveraging experience in lazy search"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-5 (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"
    - "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"
    - "deeply informed neural sampling for robot motion planning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "hallucinative topological memory for zero shot visual planning"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "model based reinforcement learning for atari"
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-8 (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"
    - "deep sequential models for sampling based 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)