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: "pves position velocity encoders for unsupervised learning of structured state representations"
  Abstract: We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects. PVEs encode a single image into a low-dimensional position state and compute the velocity state from finite differences in position. In contrast to autoencoders, position-velocity encoders are not trained by image reconstruction, but by making the position-velocity representation consistent with priors about interacting with the physical world. We applied PVEs to several simulated control tasks from pixels and achieved promising preliminary results.

Connected through hyperedge HE-1 (induced by source paper "unsupervised state representation learning in atari"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "on variational bounds of mutual information"
    - "multi object representation learning with iterative variational inference"
    - "wasserstein dependency measure for representation learning"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"
    - "object oriented dynamics predictor"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-2 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-3 (induced by source paper "adversarial skill networks unsupervised robot skill learning from video"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "monet unsupervised scene decomposition and representation"
    - "adaptive curriculum generation from demonstrations for sim to real visuomotor control"
    - "hierarchical reinforcement learning for concurrent discovery of compound and composable policies"
    - "unsupervised visuomotor control through distributional planning networks"
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "learning actionable representations from visual observations"

Connected through hyperedge HE-4 (induced by source paper "supervise thyself examining self supervised representations in interactive environments"):
  with members:
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "state representation learning for control an overview"
    - "deep contextualized word representations"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-5 (induced by source paper "state representation learning from demonstration"):
  with members:
    - "weight agnostic neural networks"
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "deepmind control suite"
    - "world models"
    - "state representation learning for control an overview"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-6 (induced by source paper "towards object detection from motion"):
  with members:
    - "online object representations with contrastive learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning instance segmentation by interaction"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "time contrastive networks self supervised learning from video"
    - "weakly supervised semantic segmentation using motion cues"
    - "object centric representation learning from unlabeled videos"
    - "learning features by watching objects move"

Connected through hyperedge HE-7 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"

Connected through hyperedge HE-8 (induced by source paper "universal planning networks"):
  with members:
    - "mpc inspired neural network policies for sequential decision making"
    - "zero shot visual imitation"
    - "hindsight experience replay"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "value prediction network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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)