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: "eye in painting with exemplar generative adversarial networks"
  Abstract: This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in painting results. We propose using exemplar information in the form of a reference image of the region to in-paint, or a perceptual code describing that object. Unlike previous conditional GAN formulations, this extra information can be inserted at multiple points within the adversarial network, thus increasing its descriptive power. We show that ExGANs can produce photo-realistic personalized in-painting results that are both perceptually and semantically plausible by applying them to the task of closed to-open eye in-painting in natural pictures. A new benchmark dataset is also introduced for the task of eye in-painting for future comparisons.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-3 (induced by source paper "auto embedding generative adversarial networks for high resolution image synthesis"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on self modulation for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "edgeconnect generative image inpainting with adversarial edge learning"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "contextual based image inpainting infer match and translate"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "effects of blur and deblurring to visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "real time mdnet"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "scale recurrent network for deep image deblurring"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-6 (induced by source paper "mask guided portrait editing with conditional gans"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-7 (induced by source paper "coherent semantic attention for image inpainting"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "the relativistic discriminator a key element missing from standard gan"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "free form image inpainting with gated convolution"
    - "shift net image inpainting via deep feature rearrangement"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "generative image inpainting with contextual attention"
    - "generative face completion"

Connected through hyperedge HE-8 (induced by source paper "smart deep copy paste"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "a style based generator architecture for generative adversarial networks"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "deep bilateral learning for real time image enhancement"
    - "real time user guided image colorization with learned deep priors"

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)