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 based on semi discrete frames"
  Abstract: Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications. The mathematical analysis of these networks was pioneered by Mallat, 2012. Specifically, Mallat considered so-called scattering networks based on identical semi-discrete wavelet frames in each network layer, and proved translation-invariance as well as deformation stability of the resulting feature extractor. The purpose of this paper is to develop Mallat's theory further by allowing for different and, most importantly, general semi-discrete frames (such as, e.g., Gabor frames, wavelets, curvelets, shearlets, ridgelets) in distinct network layers. This allows to extract wider classes of features than point singularities resolved by the wavelet transform. Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat. For Mallat's wavelet-based feature extractor, we get rid of a number of technical conditions. The mathematical engine behind our results is continuous frame theory, which allows us to completely detach the invariance and deformation stability proofs from the particular algebraic structure of the underlying frames.

Connected through hyperedge HE-1 (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"
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "joint time frequency scattering for audio classification"
    - "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-2 (induced by source paper "on lipschitz bounds of general convolutional neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "lipschitz properties for deep convolutional networks"
    - "improved training of wasserstein gans"
    - "universal adversarial perturbations"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-3 (induced by source paper "lipschitz properties for deep convolutional networks"):
  with members:
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "intriguing properties of neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "underwater object classification using scattering transform of sonar signals"):
  with members:
    - "discrete deep feature extraction a theory and new architectures"
    - "unsupervised deep haar scattering on graphs"

Connected through hyperedge HE-5 (induced by source paper "pcanet an energy perspective"):
  with members:
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "a deep graph embedding network model for face recognition"
    - "image classification with a deep network model based on compressive sensing"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "invariant scattering convolution networks"

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"
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "deep residual learning for image recognition"
    - "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.LG(Machine Learning)