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: "adversarial nets with perceptual losses for text to image synthesis"
  Abstract: Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated images often expose visible flaws that lack structural definition for an object of interest. In this paper, we aim to extend state of the art for GAN-based text-to-image synthesis by improving perceptual quality of generated images. Differentiated from previous work, our synthetic image generator optimizes on perceptual loss functions that measure pixel, feature activation, and texture differences against a natural image. We present visually more compelling synthetic images of birds and flowers generated from text descriptions in comparison to some of the most prominent existing work.

Connected through hyperedge HE-1 (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-2 (induced by source paper "unsupervised image translation using adversarial networks for improved plant disease recognition"):
  with members:
    - "generative adversarial networks an overview"
    - "nima neural image assessment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-3 (induced by source paper "cpgan full spectrum content parsing generative adversarial networks for text to image synthesis"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "memory attended recurrent network for video captioning"
    - "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"
    - "automatic stance detection using end to end memory networks"
    - "yolov3 an incremental improvement"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "picture what you read"):
  with members:
    - "aiding intra text representations with visual context for multimodal named entity recognition"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "image and encoded text fusion for multi modal classification"
    - "efficient large scale multi modal classification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-5 (induced by source paper "photographic text to image synthesis with a hierarchically nested adversarial network"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "tell me where to look guided attention inference network"
    - "context encoding for semantic segmentation"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"

Connected through hyperedge HE-6 (induced by source paper "semantic aware grad gan for virtual to real urban scene adaption"):
  with members:
    - "demystifying neural style transfer"
    - "pixelnn example based image synthesis"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "squeeze and excitation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "inferring semantic layout for hierarchical text to image synthesis"):
  with members:
    - "perceptual adversarial networks for image to image transformation"
    - "learning to generate long term future via hierarchical prediction"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "photographic image synthesis with cascaded refinement networks"
    - "a neural representation of sketch drawings"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning of semantic relevance in text to image synthesis"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "adversarial generation of natural language"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "tac gan text conditioned auxiliary classifier generative adversarial network"
    - "language generation with recurrent generative adversarial networks without pre training"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "learning what and where to draw"

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)