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: "manipulation by feel touch based control with deep predictive models"
  Abstract: Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging. General purpose control techniques that are able to effectively leverage tactile sensing as well as accurate physics models of contacts and forces remain largely elusive, and it is unclear how to even specify a desired behavior in terms of tactile percepts. In this paper, we take a step towards addressing these issues by combining high-resolution tactile sensing with data-driven modeling using deep neural network dynamics models. We propose deep tactile MPC, a framework for learning to perform tactile servoing from raw tactile sensor inputs, without manual supervision. We show that this method enables a robot equipped with a GelSight-style tactile sensor to manipulate a ball, analog stick, and 20-sided die, learning from unsupervised autonomous interaction and then using the learned tactile predictive model to reposition each object to user-specified configurations, indicated by a goal tactile reading. Videos, visualizations and the code are available here: this https URL

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"
    - "reasoning about physical interactions with object oriented prediction and planning"

Connected through hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands"):
  with members:
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "scene level pose estimation for multiple instances of densely packed objects"
    - "learning joint reconstruction of hands and manipulated objects"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "principal components of touch"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-3 (induced by source paper "optimal deep learning for robot touch"):
  with members:
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "deep learning for tactile understanding from visual and haptic data"

Connected through hyperedge HE-4 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback"):
  with members:
    - "tactile mapping and localization from high resolution tactile imprints"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "in hand object stabilization by independent finger control"
    - "in hand manipulation via motion cones"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "reactive planar manipulation with convex hybrid mpc"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-5 (induced by source paper "omnitact a multi directional high resolution touch sensor"):
  with members:
    - "accurate contact localization and indentation depth prediction with an optics based tactile sensor"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"
    - "time reversal as self supervision"

Connected through hyperedge HE-7 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"

Connected through hyperedge HE-8 (induced by source paper "improvisation through physical understanding using novel objects as tools with visual foresight"):
  with members:
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "zero shot visual imitation"
    - "deep imitative models for flexible inference planning and control"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "stochastic adversarial video prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"
    - "visual robot task 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)