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: "improved gelsight tactile sensor for measuring geometry and slip"
  Abstract: A GelSight sensor uses an elastomeric slab covered with a reflective membrane to measure tactile signals. It measures the 3D geometry and contact force information with high spacial resolution, and successfully helped many challenging robot tasks. A previous sensor [1], based on a semi-specular membrane, produces high resolution but with limited geometry accuracy. In this paper, we describe a new design of GelSight for robot gripper, using a Lambertian membrane and new illumination system, which gives greatly improved geometric accuracy while retaining the compact size. We demonstrate its use in measuring surface normals and reconstructing height maps using photometric stereo. We also use it for the task of slip detection, using a combination of information about relative motions on the membrane surface and the shear distortions. Using a robotic arm and a set of 37 everyday objects with varied properties, we find that the sensor can detect translational and rotational slip in general cases, and can be used to improve the stability of the grasp.

Connected through hyperedge HE-1 (induced by source paper "omnitact a multi directional high resolution touch sensor"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "accurate contact localization and indentation depth prediction with an optics based tactile sensor"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "design of a fully actuated robotic hand with multiple gelsight tactile sensors"):
  with members:
    - "contactdb analyzing and predicting grasp contact via thermal imaging"

Connected through hyperedge HE-3 (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"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "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"
    - "toward multimodal image to image translation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "cross modal scene networks"

Connected through hyperedge HE-4 (induced by source paper "deep gated multi modal learning in hand object pose changes estimation using tactile and image data"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "bop benchmark for 6d object pose estimation"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "segmentation driven 6d object pose estimation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "robust deep multi modal learning based on gated information fusion network"
    - "interactively picking real world objects with unconstrained spoken language instructions"

Connected through hyperedge HE-5 (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"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "the feeling of success does touch sensing help predict grasp outcomes"

Connected through hyperedge HE-6 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand"):
  with members:
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "in hand object stabilization by independent finger control"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "exoskeleton covered soft finger with vision based proprioception and exteroception"):
  with members:
    - "real time soft robot 3d proprioception via deep vision based sensing"
    - "semantic image synthesis with spatially adaptive normalization"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "bodily aware soft robots integration of proprioceptive and exteroceptive sensors"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "connecting look and feel associating the visual and tactile properties of physical materials"

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)