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: "more than a feeling learning to grasp and regrasp using vision and touch"
  Abstract: For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this letter, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model - a deep, multimodal convolutional network - predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at 1) estimating grasp adjustment outcomes, 2) selecting efficient grasp adjustments for quick grasping, and 3) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.

Connected through hyperedge HE-1 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "learning dexterous in hand manipulation"
    - "leveraging contact forces for learning to grasp"
    - "learning whole body motor skills for humanoids"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "proximal policy optimization algorithms"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "concrete problems in ai safety"

Connected through hyperedge HE-2 (induced by source paper "learning the sense of touch in simulation a sim to real strategy for vision based tactile sensing"):
  with members:
    - "ground truth force distribution for learning based tactile sensing a finite element approach"
    - "mat multi fingered adaptive tactile grasping via deep reinforcement learning"
    - "tactile based insertion for dense box packing"
    - "robust learning of tactile force estimation through robot interaction"
    - "transfer learning for vision based tactile sensing"
    - "fast optical flow using dense inverse search"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "optimal deep learning for robot touch"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "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 "omnitact a multi directional high resolution touch sensor"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "accurate contact localization and indentation depth prediction with an optics based tactile sensor"
    - "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-5 (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"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "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-6 (induced by source paper "contextual reinforcement learning of visuo tactile multi fingered grasping policies"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "generating grasp poses for a high dof gripper using neural networks"
    - "sim to real transfer for biped locomotion"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "modeling grasp type improves learning based grasp planning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "learning robotic assembly from cad"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"

Connected through hyperedge HE-7 (induced by source paper "replab a reproducible low cost arm benchmark platform for robotic learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "addressing function approximation error in actor critic methods"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "deep reinforcement learning that matters"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"

Connected through hyperedge HE-8 (induced by source paper "deep robotic prediction with hierarchical rgb d fusion"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "learning robotic assembly from cad"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "grasp pose detection in point clouds"
    - "learning end to end multimodal sensor policies for autonomous navigation"

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)