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: "discrete deep feature extraction a theory and new architectures"
  Abstract: First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and B\"olcskei, 2015. This paper considers the discrete case, introduces new convolutional neural network architectures, and proposes a mathematical framework for their analysis. Specifically, we establish deformation and translation sensitivity results of local and global nature, and we investigate how certain structural properties of the input signal are reflected in the corresponding feature vectors. Our theory applies to general filters and general Lipschitz-continuous non-linearities and pooling operators. Experiments on handwritten digit classification and facial landmark detection---including feature importance evaluation---complement the theoretical findings.

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"
    - "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-2 (induced by source paper "basic filters for convolutional neural networks training or design"):
  with members:
    - "energy propagation in deep convolutional neural networks"
    - "deep convolutional neural networks on cartoon functions"
    - "adam a method for stochastic optimization"
    - "deep scattering spectrum"
    - "unsupervised learning of invariant representations in hierarchical architectures"

Connected through hyperedge HE-3 (induced by source paper "underwater object classification using scattering transform of sonar signals"):
  with members:
    - "deep convolutional neural networks based on semi discrete frames"
    - "unsupervised deep haar scattering on graphs"

Connected through hyperedge HE-4 (induced by source paper "deep structured features for semantic segmentation"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "energy efficient machine learning in silicon a communications inspired approach"
    - "learning both weights and connections for efficient neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-5 (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"
    - "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.LG(Machine Learning)