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: "patch based image inpainting with generative adversarial networks"
  Abstract: Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks. However, current network solutions still introduce undesired artifacts and noise to the repaired regions. We present an image inpainting method that is based on the celebrated generative adversarial network (GAN) framework. The proposed PGGAN method includes a discriminator network that combines a global GAN (G-GAN) architecture with a patchGAN approach. PGGAN first shares network layers between G-GAN and patchGAN, then splits paths to produce two adversarial losses that feed the generator network in order to capture both local continuity of image texture and pervasive global features in images. The proposed framework is evaluated extensively, and the results including comparison to recent state-of-the-art demonstrate that it achieves considerable improvements on both visual and quantitative evaluations.

Connected through hyperedge HE-1 (induced by source paper "reusing discriminators for encoding towards unsupervised image to image translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "travelgan image to image translation by transformation vector learning"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-3 (induced by source paper "sean image synthesis with semantic region adaptive normalization"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "controllable artistic text style transfer via shape matching gan"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mask guided portrait editing with conditional gans"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "non stationary texture synthesis by adversarial expansion"

Connected through hyperedge HE-4 (induced by source paper "image2stylegan how to edit the embedded images"):
  with members:
    - "seeing what a gan cannot generate"
    - "detecting overfitting of deep generative networks via latent recovery"
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot video to video synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "forknet multi branch volumetric semantic completion from a single depth image"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "view volume network for semantic scene completion from a single depth image"
    - "deep learned full 3d object completion from single view"
    - "adversarial semantic scene completion from a single depth image"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "semantic foreground inpainting from weak supervision"):
  with members:
    - "privacy protection in street view panoramas using depth and multi view imagery"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "free form image inpainting with gated convolution"
    - "generative image inpainting with contextual attention"
    - "generative face completion"

Connected through hyperedge HE-7 (induced by source paper "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"):
  with members:
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "generative face completion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-8 (induced by source paper "pixel wise conditioning of generative adversarial networks"):
  with members:
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "conditional generative adversarial nets"

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)