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: "active clothing material perception using tactile sensing and deep learning"
  Abstract: Humans represent the objects in the same category using their properties, in order to well discriminate and understand them, and an intelligent robot should be able to do the same. In this work, we propose a robot system that can automatically perceive the object properties through touch. We work on the common object category of clothing. The robot moves under the guidance of an external Kinect sensor, and squeezes the clothes with a GelSight tactile sensor, then it recognizes the 11 properties of the clothing according to the tactile data. The target properties are the physical properties, like thickness, fuzziness, softness and endurance, and semantic properties like wearing season and preferred washing methods. We collect a dataset of 153 varied pieces of clothes, and make 6616 exploring iterations on them. To learn the useful information from the high-dimensional sensory output, we applied Convolutional Neural Networks (CNN) on the tactile data for recognizing the clothing properties, and on the Kinect depth images for selecting exploration locations. Experiments show that using the trained neural networks, the robot can automatically explore the unknown clothes and learn their properties. This work proposes a new architecture for active tactile perception system with vision-touch system, and has potential to enable robots to help humans with varied clothing related housework.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "deep learning for tactile understanding from visual and haptic data"

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)