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 bayesian perspective on the deep image prior"
  Abstract: The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradient descent is performed to adjust network parameters to make the output match observations. This approach yields good performance on a range of image reconstruction tasks. We show that the deep image prior is asymptotically equivalent to a stationary Gaussian process prior in the limit as the number of channels in each layer of the network goes to infinity, and derive the corresponding kernel. This informs a Bayesian approach to inference. We show that by conducting posterior inference using stochastic gradient Langevin we avoid the need for early stopping, which is a drawback of the current approach, and improve results for denoising and impainting tasks. We illustrate these intuitions on a number of 1D and 2D signal reconstruction tasks.

Connected through hyperedge HE-1 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "deep geometric prior for surface reconstruction"

Connected through hyperedge HE-2 (induced by source paper "deepred deep image prior powered by red"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "image restoration using total variation regularized deep image prior"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deep image prior"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-3 (induced by source paper "computational mirrors blind inverse light transport by deep matrix factorization"):
  with members:
    - "neural blind deconvolution using deep priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "blind image deconvolution using deep generative priors"
    - "deep image prior"
    - "optically lightweight tracking of objects around a corner"
    - "a deep matrix factorization method for learning attribute representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the spectral bias of the deep image prior"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "training behavior of deep neural network in frequency domain"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "exploring generalization in deep learning"
    - "deep image prior"
    - "understanding deep learning requires rethinking generalization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "regularization by architecture a deep prior approach for inverse problems"):
  with members:
    - "deepred deep image prior powered by red"
    - "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)