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: "deep learning for surface material classification using haptic and visual information"
  Abstract: When a user scratches a hand-held rigid tool across an object surface, an acceleration signal can be captured, which carries relevant information about the surface. More importantly, such a haptic signal is complementary to the visual appearance of the surface, which suggests the combination of both modalities for the recognition of the surface material. In this paper, we present a novel deep learning method dealing with the surface material classification problem based on a Fully Convolutional Network (FCN), which takes as input the aforementioned acceleration signal and a corresponding image of the surface texture. Compared to previous surface material classification solutions, which rely on a careful design of hand-crafted domain-specific features, our method automatically extracts discriminative features utilizing the advanced deep learning methodologies. Experiments performed on the TUM surface material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.

Connected through hyperedge HE-1 (induced by source paper "three dimensional convolutional neural network 3d cnn for heterogeneous material homogenization"):
  with members:
    - "advanced steel microstructural classification by deep learning methods"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "adam a method for stochastic optimization"
    - "material recognition in the wild with the materials in context database"

Connected through hyperedge HE-2 (induced by source paper "perceptnet learning perceptual similarity of haptic textures in presence of unorderable triplets"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "discriminative clustering with relative constraints"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep learning for tactile understanding from visual and haptic data"
    - "adam a method for stochastic optimization"
    - "learning multi modal similarity"

Connected through hyperedge HE-3 (induced by source paper "spi optimizer an integral separated pi controller for stochastic optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "neural optimizer search with reinforcement learning"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "an empirical analysis of the optimization of deep network loss surfaces"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)