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: "tactile regrasp grasp adjustments via simulated tactile transformations"
  Abstract: This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching for local transformations of tactile measurements that improve the quality of the grasp. First, we construct a tactile-based grasp quality metric using a deep convolutional neural network trained on over 2800 grasps. The quality of each grasp, a continuous value between 0 and 1, is determined experimentally by measuring its resistance to external perturbations. Second, we simulate the tactile imprints associated with robot motions relative to the initial grasp by performing rigid-body transformations of the given tactile measurements. The newly generated tactile imprints are evaluated with the learned grasp quality network and the regrasp action is chosen to maximize the grasp quality. #R##N#Results show that the grasp quality network can predict the outcome of grasps with an average accuracy of 85% on known objects and 75% on a cross validation set of 12 objects. The regrasp control policy improves the success rate of grasp actions by an average relative increase of 70% on a test set of 8 objects.

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"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "leveraging contact forces for learning to grasp"
    - "learning whole body motor skills for humanoids"
    - "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 "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"
    - "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"
    - "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-3 (induced by source paper "mat multi fingered adaptive tactile grasping via deep reinforcement learning"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "modeling grasp type improves learning based grasp planning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "learning to grasp without seeing"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "leveraging contact forces for learning to grasp"

Connected through hyperedge HE-4 (induced by source paper "learning to identify object instances by touch tactile recognition via multimodal matching"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "learning to grasp without seeing"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "objects that sound"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "look listen and learn"

Connected through hyperedge HE-5 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "robotic tactile perception of object properties a review"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"

Connected through hyperedge HE-6 (induced by source paper "cable manipulation with a tactile reactive gripper"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-7 (induced by source paper "more than a feeling learning to grasp and regrasp using vision and touch"):
  with members:
    - "learning to grasp without seeing"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "estimating tactile data for adaptive grasping of novel objects"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-8 (induced by source paper "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"):
  with members:
    - "voronoi features for tactile sensing direct inference of pressure shear and contact locations"
    - "the limits and potentials of deep learning for robotics"
    - "principal components of touch"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "slip detection with combined tactile and visual information"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"

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)