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: "localizing the object contact through matching tactile features with visual map"
  Abstract: This paper presents a novel framework for integration of vision and tactile sensing by localizing tactile readings in a visual object map. Intuitively, there are some correspondences, e.g., prominent features, between visual and tactile object identification. To apply it in robotics, we propose to localize tactile readings in visual images by sharing same sets of feature descriptors through two sensing modalities. It is then treated as a probabilistic estimation problem solved in a framework of recursive Bayesian filtering. Feature-based measurement model and Gaussian based motion model are thus built. In our tests, a tactile array sensor is utilized to generate tactile images during interaction with objects and the results have proven the feasibility of our proposed framework.

Connected through hyperedge HE-1 (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"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "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"

Connected through hyperedge HE-2 (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"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "robotic tactile perception of object properties a review"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"

Connected through hyperedge HE-3 (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"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"

Connected through hyperedge HE-4 (induced by source paper "3d shape perception from monocular vision touch and shape priors"):
  with members:
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "iclap shape recognition by combining proprioception and touch sensing"):
  with members:
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "vitac feature sharing between vision and tactile sensing for cloth texture recognition"):
  with members:
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "iterative closest labeled point for tactile object shape recognition"
    - "the curious robot learning visual representations via physical interactions"
    - "visually indicated sounds"

Connected through hyperedge HE-7 (induced by source paper "robotic tactile perception of object properties a review"):
  with members:
    - "ergodic exploration using binary sensing for nonparametric shape estimation"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "knock knock acoustic object recognition by using stacked denoising autoencoders"
    - "memory unscented particle filter for 6 dof tactile localization"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "iterative closest labeled point for tactile object shape recognition"
    - "deep learning for tactile understanding from visual and haptic data"
    - "representation learning a review and new perspectives"

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)