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: "invertible generative models for inverse problems mitigating representation error and dataset bias"
  Abstract: Trained generative models have shown remarkable performance as priors for inverse problems in imaging. For example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training dataset. In this paper, we demonstrate that invertible neural networks, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting. Our formulation is an empirical risk minimization that does not directly optimize the likelihood of images, as one would expect. Instead we optimize the likelihood of the latent representation of images as a proxy, as this is empirically easier. For compressive sensing, our formulation can yield higher accuracy than sparsity priors across almost all undersampling ratios. For the same accuracy on test images, they can use 10-20x fewer measurements. We demonstrate that invertible priors can yield better reconstructions than sparsity priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images.

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"
    - "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"
    - "robust compressive phase retrieval via deep generative priors"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing with invertible generative models and dependent noise"):
  with members:
    - "deep compressed sensing"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "invertible residual networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "density estimation using real nvp"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-3 (induced by source paper "reducing the representation error of gan image priors using the deep decoder"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "energy based generative adversarial network"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

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)