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: "connecting look and feel associating the visual and tactile properties of physical materials"
  Abstract: For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs.

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"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "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 "connecting touch and vision via cross modal prediction"):
  with members:
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "toward multimodal image to image translation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "cross modal scene networks"

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"
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "objects that sound"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "look listen and learn"

Connected through hyperedge HE-4 (induced by source paper "visual tactile fusion object clustering"):
  with members:
    - "representative task self selection for flexible clustered lifelong learning"
    - "semantic transferable weakly supervised endoscopic lesions segmentation"
    - "lifelong spectral clustering"
    - "deep spectral clustering using dual autoencoder network"
    - "connecting touch and vision via cross modal prediction"
    - "3d shape perception from monocular vision touch and shape priors"
    - "active clothing material perception using tactile sensing and deep learning"
    - "deep learning for tactile understanding from visual and haptic data"

Connected through hyperedge HE-5 (induced by source paper "learning an action conditional model for haptic texture generation"):
  with members:
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "interactive perception leveraging action in perception and perception in action"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "effective estimation of contact force and torque for vision based tactile sensor with helmholtz hodge decomposition"):
  with members:
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "slip detection analysis and calibration of univariate tactile signals"
    - "improved gelsight tactile sensor for measuring geometry and slip"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "more than a feeling learning to grasp and regrasp using vision and touch"):
  with members:
    - "learning to grasp without seeing"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "estimating tactile data for adaptive grasping of novel objects"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

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.CV(Computer Vision and Pattern Recognition)