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: "slip detection with combined tactile and visual information"
  Abstract: Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03% is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine 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"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "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"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "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:
    - "fingervision tactile sensor design and slip detection using convolutional lstm network"
    - "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 "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"):
  with members:
    - "voronoi features for tactile sensing direct inference of pressure shear and contact locations"
    - "the limits and potentials of deep learning for robotics"
    - "principal components of touch"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "fingervision tactile sensor design and slip detection using convolutional lstm network"):
  with members:
    - "slip detection analysis and calibration of univariate tactile signals"
    - "an analysis of deep neural network models for practical applications"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)