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: "an introduction to image synthesis with generative adversarial nets"
  Abstract: There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years. Proposed in 2014, GAN has been applied to various applications such as computer vision and natural language processing, and achieves impressive performance. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already demonstrated the great potential of using GAN in image synthesis. In this paper, we provide a taxonomy of methods used in image synthesis, review different models for text-to-image synthesis and image-to-image translation, and discuss some evaluation metrics as well as possible future research directions in image synthesis with GAN.

Connected through hyperedge HE-1 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-2 (induced by source paper "a survey and taxonomy of adversarial neural networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "how generative adversarial networks and their variants work an overview"
    - "mirrorgan learning text to image generation by redescription"
    - "dm gan dynamic memory generative adversarial networks for text to image synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-3 (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"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-4 (induced by source paper "lpaintb learning to paint from self supervision"):
  with members:
    - "paintbot a reinforcement learning approach for natural media painting"
    - "revisiting self supervised visual representation learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "flow grounded spatial temporal video prediction from still images"
    - "unsupervised representation learning by predicting image rotations"
    - "hindsight experience replay"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "paintbot a reinforcement learning approach for natural media painting"):
  with members:
    - "flow grounded spatial temporal video prediction from still images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "universal style transfer via feature transforms"
    - "proximal policy optimization algorithms"
    - "a neural representation of sketch drawings"
    - "perceptual losses for real time style transfer and super resolution"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "generating videos with scene dynamics"

Connected through hyperedge HE-6 (induced by source paper "interactive image generation using scene graphs"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "text2scene generating compositional scenes from textual descriptions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "generating image sequence from description with lstm conditional gan"
    - "improved image captioning via policy gradient optimization of spider"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "fully automated image de fencing using conditional generative adversarial networks"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "my camera can see through fences a deep learning approach for image de fencing"
    - "generative image inpainting with contextual attention"
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "generative adversarial text to image synthesis"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "automated augmentation with reinforcement learning and gans for robust identification of traffic signs using front camera images"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "style separation and synthesis via generative adversarial networks"
    - "autoaugment learning augmentation policies from data"
    - "a style based generator architecture for generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dataset augmentation with synthetic images improves semantic segmentation"
    - "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)