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: "iclap shape recognition by combining proprioception and touch sensing"
  Abstract: For humans, both the proprioception and touch sensing are highly utilized when performing haptic perception. However, most approaches in robotics use only either proprioceptive data or touch data in haptic object recognition. In this paper, we present a novel method named Iterative Closest Labeled Point (iCLAP) to link the kinesthetic cues and tactile patterns fundamentally and also introduce its extensions to recognize object shapes. In the training phase, the iCLAP first clusters the features of tactile readings into a codebook and assigns these features with distinct label numbers. A 4D point cloud of the object is then formed by taking the label numbers of the tactile features as an additional dimension to the 3D sensor positions; hence, the two sensing modalities are merged to achieve a synthesized perception of the touched object. Furthermore, we developed and validated hybrid fusion strategies, product based and weighted sum based, to combine decisions obtained from iCLAP and single sensing modalities. Extensive experimentation demonstrates a dramatic improvement of object recognition using the proposed methods and it shows great potential to enhance robot perception ability.

Connected through hyperedge HE-1 (induced by source paper "touching to see and seeing to feel robotic cross modal sensorydata generation for visual tactile perception"):
  with members:
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "localizing the object contact through matching tactile features with visual map"

Connected through hyperedge HE-2 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "robotic tactile perception of object properties a review"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"

Connected through hyperedge HE-3 (induced by source paper "garmnet improving global with local perception for robotic laundry folding"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "surface following using deep reinforcement learning and a gelsighttactile sensor"):
  with members:
    - "vitac feature sharing between vision and tactile sensing for cloth texture recognition"
    - "robotic tactile perception of object properties a review"
    - "iterative closest labeled point for tactile object shape recognition"
    - "localizing the object contact through matching tactile features with visual map"

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)