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: "robust compressive phase retrieval via deep generative priors"
  Abstract: This paper proposes a new framework to regularize the highly ill-posed and non-linear phase retrieval problem through deep generative priors using simple gradient descent algorithm. We experimentally show effectiveness of proposed algorithm for random Gaussian measurements (practically relevant in imaging through scattering media) and Fourier friendly measurements (relevant in optical set ups). We demonstrate that proposed approach achieves impressive results when compared with traditional hand engineered priors including sparsity and denoising frameworks for number of measurements and robustness against noise. Finally, we show the effectiveness of the proposed approach on a real transmission matrix dataset in an actual application of multiple scattering media imaging.

Connected through hyperedge HE-1 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models"):
  with members:
    - "deep compressed sensing"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "invertible generative models for inverse problems mitigating representation error and dataset bias"
    - "fast and provable admm for learning with generative priors"
    - "blind phaseless short time fourier transform recovery"
    - "deep ptych subsampled fourier ptychography using generative priors"
    - "blind image deconvolution using deep generative priors"
    - "phase retrieval under a generative prior"

Connected through hyperedge HE-2 (induced by source paper "denoising and regularization via exploiting the structural bias of convolutional generators"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "rate optimal denoising with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "using black box compression algorithms for phase retrieval"):
  with members:
    - "linear spectral estimators and an application to phase retrieval"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "the little engine that could regularization by denoising red"
    - "phasemax convex phase retrieval via basis pursuit"
    - "compression based compressed sensing"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"):
  with members:
    - "phase retrieval under a generative prior"
    - "began boundary equilibrium generative adversarial networks"
    - "image restoration by iterative denoising and backward projections"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "learning deep cnn denoiser prior for image restoration"
    - "sparse phase retrieval via truncated amplitude flow"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-5 (induced by source paper "blind image deconvolution using pretrained generative priors"):
  with members:
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "deep ptych subsampled fourier ptychography using generative priors"
    - "learning a discriminative prior for blind image deblurring"
    - "latent convolutional models"
    - "phase retrieval under a generative prior"
    - "progressive growing of gans for improved quality stability and variation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"

Connected through hyperedge HE-6 (induced by source paper "low shot learning with untrained neural networks for imaging inverse problems"):
  with members:
    - "deep compressed sensing"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "blind image deconvolution using deep generative priors"
    - "phase retrieval under a generative prior"
    - "deep image prior"

Connected through hyperedge HE-7 (induced by source paper "deep ptych subsampled fourier ptychography using generative priors"):
  with members:
    - "deep learning approach for fourier ptychography microscopy"
    - "phase retrieval under a generative prior"
    - "single image super resolution based on wiener filter in similarity domain"
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "on fienup methods for sparse phase retrieval"
    - "progressive growing of gans for improved quality stability and variation"
    - "fourier phase retrieval uniqueness and algorithms"
    - "global guarantees for enforcing deep generative priors by empirical risk"

Connected through hyperedge HE-8 (induced by source paper "deep decoder concise image representations from untrained non convolutional networks"):
  with members:
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "the little engine that could regularization by denoising red"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "variable rate image compression with recurrent neural 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)