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: "semantic image inpainting with perceptual and contextual losses"
  Abstract: In this paper, we propose a novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network (DCGAN). We define a loss function consisting of two parts: (1) a contextual loss that preserves similarity between the input corrupted image and the recovered image, and (2) a perceptual loss that ensures a perceptually realistic output image. Given a corrupted image with missing values, we use back-propagation on this loss to map the corrupted image to a smaller latent space. The mapped vector is then passed through the generative model to predict the missing content. The proposed framework is evaluated on the CelebA and SVHN datasets for two challenging inpainting tasks with random 80% corruption and large blocky corruption. Experiments show that our method can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.

Connected through hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-2 (induced by source paper "piigan generative adversarial networks for pluralistic image inpainting"):
  with members:
    - "learning pyramid context encoder network for high quality image inpainting"
    - "coherent semantic attention for image inpainting"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "high resolution face completion with multiple controllable attributes via fully end to end progressive generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-4 (induced by source paper "connecting gans and mfgs"):
  with members:
    - "smoothness and stability in gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "recent advances of image steganography with generative adversarial networks"):
  with members:
    - "steganogan high capacity image steganography with gans"
    - "cnn based adversarial embedding for image steganography"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "spatial image steganography based on generative adversarial network"
    - "adaptive spatial steganography based on probability controlled adversarial examples"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "recovering faces from portraits with auxiliary facial attributes"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-7 (induced by source paper "dual generator generative adversarial networks for multi domain image to image translation"):
  with members:
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "every smile is unique landmark guided diverse smile generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "generative face completion"

Connected through hyperedge HE-8 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative 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.CV(Computer Vision and Pattern Recognition)