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: "a tale of two bases local nonlocal regularization on image patches with convolution framelets"
  Abstract: We propose an image representation scheme combining the local and nonlocal characterization of patches in an image. Our representation scheme can be shown to be equivalent to a tight frame constructed from convolving local bases (e.g. wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g. spectral basis induced by nonlinear dimension reduction on patches), and we call the resulting frame elements {\it convolution framelets}. Insight gained from analyzing the proposed representation leads to a novel interpretation of a recent high-performance patch-based image inpainting algorithm using Point Integral Method (PIM) and Low Dimension Manifold Model (LDMM) [Osher, Shi and Zhu, 2016]. In particular, we show that LDMM is a weighted $\ell_2$-regularization on the coefficients obtained by decomposing images into linear combinations of convolution framelets; based on this understanding, we extend the original LDMM to a reweighted version that yields further improved inpainting results. In addition, we establish the energy concentration property of convolution framelet coefficients for the setting where the local basis is constructed from a given nonlocal basis via a linear reconstruction framework; a generalization of this framework to unions of local embeddings can provide a natural setting for interpreting BM3D, one of the state-of-the-art image denoising algorithms.

Connected through hyperedge HE-1 (induced by source paper "understanding geometry of encoder decoder cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a differential topological view of challenges in learning with feedforward neural networks"
    - "k space deep learning for accelerated mri"
    - "dnn or k nn that is the generalize vs memorize question"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"

Connected through hyperedge HE-2 (induced by source paper "photorealistic style transfer via wavelet transforms"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer with deep feature reshuffle"
    - "nsml meet the mlaas platform with a real world case study"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "scaling the scattering transform deep hybrid networks"
    - "wavelet convolutional neural networks for texture classification"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-3 (induced by source paper "non local recurrent network for image restoration"):
  with members:
    - "image super resolution via dual state recurrent networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "non local neural networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "attention is all you need"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "a simple neural network module for relational reasoning"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

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)