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 convolutional neural networks on cartoon functions"
  Abstract: Wiatowski and Bolcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities. While the translation invariance result applies to square-integrable functions, the deformation stability bound holds for band-limited functions only. Many signals of practical relevance (such as natural images) exhibit, however, sharp and curved discontinuities and are hence not band-limited. The main contribution of this paper is a deformation stability result that takes these structural properties into account. Specifically, we establish deformation stability bounds for the class of cartoon functions introduced by Donoho, 2001.

Connected through hyperedge HE-1 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks"):
  with members:
    - "deep network approximation for smooth functions"
    - "the troublesome kernel why deep learning for inverse problems is typically unstable"
    - "greedy shallow networks a new approach for constructing and training neural networks"
    - "deep double descent where bigger models and more data hurt"
    - "growing axons greedy learning of neural networks with application to function approximation"
    - "a polynomial based approach for architectural design and learning with deep neural networks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "neural network integral representations with the relu activation function"

Connected through hyperedge HE-2 (induced by source paper "gabor frames and deep scattering networks in audio processing"):
  with members:
    - "machines listening to music the role of signal representations in learning from music"
    - "understanding deep learning requires rethinking generalization"
    - "discrete deep feature extraction a theory and new architectures"
    - "joint time frequency scattering for audio classification"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "energy propagation in deep convolutional neural networks"):
  with members:
    - "lipschitz properties for deep convolutional networks"
    - "deep structured features for semantic segmentation"
    - "deep residual learning for image recognition"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "rigid motion scattering for texture classification"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "basic filters for convolutional neural networks training or design"):
  with members:
    - "energy propagation in deep convolutional neural networks"
    - "discrete deep feature extraction a theory and new architectures"
    - "adam a method for stochastic optimization"
    - "deep scattering spectrum"
    - "unsupervised learning of invariant representations in hierarchical architectures"

Connected through hyperedge HE-5 (induced by source paper "discrete deep feature extraction a theory and new architectures"):
  with members:
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "deep roto translation scattering for object classification"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "a mathematical theory of deep convolutional neural networks for feature extraction"):
  with members:
    - "energy propagation in deep convolutional neural networks"
    - "discrete deep feature extraction a theory and new architectures"
    - "deep residual learning for image recognition"
    - "deep convolutional neural networks based on semi discrete frames"
    - "rigid motion scattering for texture classification"
    - "deep roto translation scattering for object classification"
    - "deep scattering spectrum"
    - "invariant scattering convolution networks"

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.IT(Information Theory)