human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"):
  Abstract: Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniques from vision to be applied to touch. In this paper, we apply deep learning to an optical biomimetic tactile sensor, the TacTip, which images an array of papillae (pins) inside its sensing surface analogous to structures within human skin. Our main result is that the application of a deep CNN can give reliable edge perception and thus a robust policy for planning contact points to move around object contours. Robustness is demonstrated over several irregular and compliant objects with both tapping and continuous sliding, using a model trained only by tapping onto a disk. These results relied on using techniques to encourage generalization to tasks beyond which the model was trained. We expect this is a generic problem in practical applications of tactile sensing that deep learning will solve. A video demonstrating the approach can be found at this https URL

Includes members:
  Member "voronoi features for tactile sensing direct inference of pressure shear and contact locations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shear invariant sliding contact perception with a soft tactile sensor")
  Member "the limits and potentials of deep learning for robotics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "principal components of touch":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "more than a feeling learning to grasp and regrasp using vision and touch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "gelslim a high resolution compact robust and calibrated tactile sensing finger":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand")
  Member "slip detection with combined tactile and visual information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tactile based insertion for dense box packing")
  Member "tactile regrasp grasp adjustments via simulated tactile transformations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "connecting look and feel associating the visual and tactile properties of physical materials":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal deep learning for robot touch")

Question: Please tell me which class the source paper 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)