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 "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception"):
  Abstract: The integration of visual-tactile stimulus is common while humans performing daily tasks. In contrast, using unimodal visual or tactile perception limits the perceivable dimensionality of a subject. However, it remains a challenge to integrate the visual and tactile perception to facilitate robotic tasks. In this paper, we propose a novel framework for the cross-modal sensory data generation for visual and tactile perception. Taking texture perception as an example, we apply conditional generative adversarial networks to generate pseudo visual images or tactile outputs from data of the other modality. Extensive experiments on the ViTac dataset of cloth textures show that the proposed method can produce realistic outputs from other sensory inputs. We adopt the structural similarity index to evaluate similarity of the generated output and real data and results show that realistic data have been generated. Classification evaluation has also been performed to show that the inclusion of generated data can improve the perception performance. The proposed framework has potential to expand datasets for classification tasks, generate sensory outputs that are not easy to access, and also advance integrated visual-tactile perception.

Includes members:
  Member "iclap shape recognition by combining proprioception and touch sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tactile mapping and localization from high resolution tactile imprints")
  Member "vitac feature sharing between vision and tactile sensing for cloth texture recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal deep learning for robot touch")
  Member "domain adaptation for visual applications a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "robotic tactile perception of object properties a review":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "iterative closest labeled point for tactile object shape recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surface following using deep reinforcement learning and a gelsighttactile sensor")
  Member "generative adversarial text to image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "context encoders feature learning by inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)