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: "latent convolutional models"
  Abstract: We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space. After training, the new model provides a strong and universal image prior for a variety of image restoration tasks such as large-hole inpainting, superresolution, and colorization. To model high-resolution natural images, our approach uses latent spaces of very high dimensionality (one to two orders of magnitude higher than previous latent image models). To tackle this high dimensionality, we use latent spaces with a special manifold structure (convolutional manifolds) parameterized by a ConvNet of a certain architecture. In the experiments, we compare the learned latent models with latent models learned by autoencoders, advanced variants of generative adversarial networks, and a strong baseline system using simpler parameterization of the latent space. Our model outperforms the competing approaches over a range of restoration tasks.

Connected through hyperedge HE-1 (induced by source paper "image processing using multi code gan prior"):
  with members:
    - "seeing what a gan cannot generate"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "few shot unsupervised image to image translation"
    - "on the steerability of generative adversarial networks"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "interpreting the latent space of gans for semantic face editing"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "invertible generative models for inverse problems mitigating representation error and dataset bias"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "analyzing inverse problems with invertible neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "finger gan generating realistic fingerprint images using connectivity imposed gan"
    - "blind image deconvolution using deep generative priors"
    - "phase retrieval under a generative prior"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"

Connected through hyperedge HE-3 (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"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "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-4 (induced by source paper "reconstruction of 3d porous media from 2d slices"):
  with members:
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"
    - "large scale shape retrieval with sparse 3d convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"
    - "numerical analysis of topological characteristics of three dimensional geological models of oil and gas fields"

Connected through hyperedge HE-5 (induced by source paper "deep image prior"):
  with members:
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "convolutional dictionary learning via local processing"
    - "benchmarking denoising algorithms with real photographs"
    - "the robust manifold defense adversarial training using generative models"
    - "zero shot super resolution using deep internal learning"
    - "photo realistic single image super resolution using a generative adversarial network"

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)