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 "optimal deep learning for robot touch"):
  Abstract: This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applied to tactile robotics, focussing on optical tactile sensors, which help bridge from deep learning for vision to touch. We then show how deep learning can be used to train accurate pose models of 3D surfaces and edges that are insensitive to nuisance variables such as motion-dependent shear. This involves including representative motions as unlabelled perturbations of the training data and using Bayesian optimization of the network and training hyperparameters to find the most accurate models. Accurate estimation of pose from touch will enable robots to safely and precisely control their physical interactions, underlying a wide range of object exploration and manipulation tasks.

Includes members:
  Member "manipulation by feel touch based control with deep predictive models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "action for better prediction")
  Member "learning to identify object instances by touch tactile recognition via multimodal matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand")
  Member "more than a feeling learning to grasp and regrasp using vision and touch":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reaching grasping and re grasping learning multimode grasping skills")
  Member "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand")
  Member "vitac feature sharing between vision and tactile sensing for cloth texture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception")
  Member "tactile regrasp grasp adjustments via simulated tactile transformations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "connecting touch and vision via cross modal prediction")
  Member "the feeling of success does touch sensing help predict grasp outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")

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)