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: "robot motion planning in learned latent spaces"
  Abstract: This paper presents Latent Sampling-based Motion Planning (L-SBMP), a methodology towards computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this paper we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where planning occurs in the visual space). Specifically, the learned latent space is constructed through an autoencoding network, a dynamics network, and a collision checking network, which mirror the three main algorithmic primitives of SBMP, namely state sampling, local steering, and collision checking. Notably, these networks can be trained through only raw data of the system's states and actions along with a supervising collision checker. Building upon these networks, an RRT-based algorithm is used to plan motions directly in the latent space - we refer to this exploration algorithm as Learned Latent RRT (L2RRT). This algorithm globally explores the latent space and is capable of generalizing to new environments. The overall methodology is demonstrated on two planning problems, namely a visual planning problem, whereby planning happens in the visual (pixel) space, and a humanoid robot planning problem.

Connected through hyperedge HE-1 (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"
    - "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"
    - "motion planning networks"
    - "pac bayes control synthesizing controllers that provably generalize to novel environments"

Connected through hyperedge HE-2 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces"):
  with members:
    - "manipulating deformable objects by interleaving prediction planning and control"
    - "learning robotic manipulation through visual planning and acting"
    - "learning latent space dynamics for tactile servoing"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "robust locally linear controllable embedding"
    - "deep visual foresight for planning robot motion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "data efficient hierarchical reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"
    - "motion planning networks"

Connected through hyperedge HE-6 (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"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-7 (induced by source paper "harnessing reinforcement learning for neural motion planning"):
  with members:
    - "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"
    - "deep sequential models for sampling based planning"

Connected through hyperedge HE-8 (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"
    - "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"
    - "deeply informed neural sampling 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)