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: "convolutional dictionary learning via local processing"
  Abstract: Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations. Although several works have addressed the dictionary learning problem under this model, these relied on an ADMM formulation in the Fourier domain, losing the sense of locality and the relation to the traditional patch-based sparse pursuit. A recent work suggested a novel theoretical analysis of this global model, providing guarantees that rely on a localized sparsity measure. Herein, we extend this local-global relation by showing how one can efficiently solve the convolutional sparse pursuit problem and train the filters involved, while operating locally on image patches. Our approach provides an intuitive algorithm that can leverage standard techniques from the sparse representations field. The proposed method is fast to train, simple to implement, and flexible enough that it can be easily deployed in a variety of applications. We demonstrate the proposed training scheme for image inpainting and image separation, while achieving state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"

Connected through hyperedge HE-2 (induced by source paper "deep likelihood network for image restoration with multiple degradations"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "the perception distortion tradeoff"
    - "on the learning dynamics of deep neural networks"
    - "physics based generative adversarial models for image restoration and beyond"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-3 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-4 (induced by source paper "online convolutional sparse coding with sample dependent dictionary"):
  with members:
    - "online convolutional dictionary learning for multimodal imaging"
    - "scalable online convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "learning feed forward one shot learners"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-5 (induced by source paper "deep image prior"):
  with members:
    - "latent convolutional models"
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "benchmarking denoising algorithms with real photographs"
    - "the robust manifold defense adversarial training using generative models"
    - "zero shot super resolution using deep internal learning"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "online convolutional dictionary learning"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "convolutional dictionary learning acceleration and convergence"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning for multimodal imaging"
    - "scalable online convolutional sparse coding"
    - "a survey of sparse representation algorithms and applications"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"

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.CV(Computer Vision and Pattern Recognition)