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: "deepred deep image prior powered by red"
  Abstract: Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be effective, its results fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DeepRED) can be merged to a highly effective recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested inverse problems.

Connected through hyperedge HE-1 (induced by source paper "block coordinate regularization by denoising"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "deep mean shift priors for image restoration"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-2 (induced by source paper "photorealistic material editing through direct image manipulation"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "single image svbrdf capture with a rendering aware deep network"
    - "gaussian material synthesis"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "dynamic routing between capsules"
    - "neural optimizer search with reinforcement learning"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-3 (induced by source paper "a projectional ansatz to reconstruction"):
  with members:
    - "regularization by architecture a deep prior approach for inverse problems"
    - "adversarial regularizers in inverse problems"
    - "deep image prior"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "towards the automation of deep image prior"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "non local recurrent network for image restoration"
    - "universal denoising networks a novel cnn architecture for image denoising"

Connected through hyperedge HE-5 (induced by source paper "regularization by architecture a deep prior approach for inverse problems"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "deep image prior"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning to learn by gradient descent by gradient descent"
    - "maximal sparsity with deep networks"
    - "understanding deep learning requires rethinking generalization"

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)