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: "fingervision tactile sensor design and slip detection using convolutional lstm network"
  Abstract: Tactile sensing is essential to the human perception system, so as to robot. In this paper, we develop a novel optical-based tactile sensor "FingerVision" with effective signal processing algorithms. This sensor is composed of soft skin with embedded marker array bonded to rigid frame, and a web camera with a fisheye lens. While being excited with contact force, the camera tracks the movements of markers and deformation field is obtained. Compared to existing tactile sensors, our sensor features compact footprint, high resolution, and ease of fabrication. Besides, utilizing the deformation field estimation, we propose a slip classification framework based on convolution Long Short Term Memory (convolutional LSTM) networks. The data collection process takes advantage of the human sense of slip, during which human hand holds 12 daily objects, interacts with sensor skin and labels data with a slip or non-slip identity based on human feeling of slip. Our slip classification framework performs high accuracy of 97.62% on the test dataset. It is expected to be capable of enhancing the stability of robot grasping significantly, leading to better contact force control, finer object interaction and more active sensing manipulation.

Connected through hyperedge HE-1 (induced by source paper "tactile based insertion for dense box packing"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "slip detection with combined tactile and visual information"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning robot activities from first person human videos using convolutional future regression"

Connected through hyperedge HE-2 (induced by source paper "towards learning to detect and predict contact events on vision based tactile sensors"):
  with members:
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "slip detection with combined tactile and visual information"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "slip detection analysis and calibration of univariate tactile signals"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "tactilegcn a graph convolutional network for predicting grasp stability with tactile sensors"):
  with members:
    - "slip detection with combined tactile and visual information"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-4 (induced by source paper "cable manipulation with a tactile reactive gripper"):
  with members:
    - "manipulation by feel touch based control with deep predictive models"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-5 (induced by source paper "effective estimation of contact force and torque for vision based tactile sensor with helmholtz hodge decomposition"):
  with members:
    - "slip detection analysis and calibration of univariate tactile signals"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "improved gelsight tactile sensor for measuring geometry and slip"

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)