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: "auto painter cartoon image generation from sketch by using conditional generative adversarial networks"
  Abstract: Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images. Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment. In this paper, we investigate the sketch-to-image synthesis problem by using conditional generative adversarial networks (cGAN). We propose the auto-painter model which can automatically generate compatible colors for a sketch. The new model is not only capable of painting hand-draw sketch with proper colors, but also allowing users to indicate preferred colors. Experimental results on two sketch datasets show that the auto-painter performs better that existing image-to-image methods.

Connected through hyperedge HE-1 (induced by source paper "sketch to art synthesizing stylized art images from sketches"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "the shape of art history in the eyes of the machine"
    - "style and content disentanglement in generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"

Connected through hyperedge HE-3 (induced by source paper "pros and cons of gan evaluation measures"):
  with members:
    - "decoupled learning for conditional adversarial networks"
    - "cross view image synthesis using conditional gans"
    - "generating adversarial examples with adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks gans what it can generate and what it cannot"):
  with members:
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "pros and cons of gan evaluation measures"
    - "evolutionary generative adversarial networks"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "comparative study on generative adversarial networks"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

Connected through hyperedge HE-5 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketchyscene richly annotated scene sketches"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "outline colorization through tandem adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "user guided deep anime line art colorization with conditional adversarial networks"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comicolorization semi automatic manga colorization"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "dilated residual networks"
    - "real time user guided image colorization with learned deep priors"

Connected through hyperedge HE-7 (induced by source paper "imagine this scripts to compositions to videos"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "learning to generate long term future via hierarchical prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "attentive semantic video generation using captions"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "aligned image word representations improve inductive transfer across vision language tasks"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-8 (induced by source paper "neural abstract style transfer for chinese traditional painting"):
  with members:
    - "stereoscopic neural style transfer"
    - "synthesizing filamentary structured images with gans"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "deep photo style transfer"
    - "laplacian steered neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"

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)