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: "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
  Abstract: Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots are also envisioned to be of such multi-modal sensing ability. In this paper, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences in the two sensing modalities, which deteriorates the perception performance. To solve this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception"):
  with members:
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "localizing the object contact through matching tactile features with visual map"

Connected through hyperedge HE-3 (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"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "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-4 (induced by source paper "surface following using deep reinforcement learning and a gelsighttactile sensor"):
  with members:
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"
    - "localizing the object contact through matching tactile features with visual map"

Connected through hyperedge HE-5 (induced by source paper "color coded fiber optic tactile sensor for an elastomeric robot skin"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "perceiving and reasoning about liquids using fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "3d shape perception from monocular vision touch and shape priors"):
  with members:
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "robotic tactile perception of object properties a review"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "interactive perception leveraging action in perception and perception in action"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "self supervised intrinsic image decomposition"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-7 (induced by source paper "iclap shape recognition by combining proprioception and touch sensing"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "iterative closest labeled point for tactile object shape recognition"
    - "localizing the object contact through matching tactile features with visual map"

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)