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: "i2t2i learning text to image synthesis with textual data augmentation"
  Abstract: Translating information between text and image is a fundamental problem in artificial intelligence that connects natural language processing and computer vision. In the past few years, performance in image caption generation has seen significant improvement through the adoption of recurrent neural networks (RNN). Meanwhile, text-to-image generation begun to generate plausible images using datasets of specific categories like birds and flowers. We've even seen image generation from multi-category datasets such as the Microsoft Common Objects in Context (MSCOCO) through the use of generative adversarial networks (GANs). Synthesizing objects with a complex shape, however, is still challenging. For example, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image (I2T2I) which integrates text-to-image and image-to-text (image captioning) synthesis to improve the performance of text-to-image synthesis. We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art. We also demonstrate that I2T2I can achieve transfer learning by using a pre-trained image captioning module to generate human images on the MPII Human Pose

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 "ftgan a fully trained generative adversarial networks for text to face generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "towards open set identity preserving face synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (induced by source paper "integrating semantic knowledge to tackle zero shot text classification"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "a large scale attribute dataset for zero shot learning"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data augmentation for low resource neural machine translation"
    - "train once test anywhere zero shot learning for text classification"
    - "data noising as smoothing in neural network language models"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-4 (induced by source paper "unsupervised annotation of phenotypic abnormalities via semantic latent representations on electronic health records"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "the deep poincar e map a novel approach for left ventricle segmentation"
    - "towards automated icd coding using deep learning"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "learning to globally edit images with textual description"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "interactive image manipulation with natural language instruction commands"
    - "cross sentence n ary relation extraction with graph lstms"
    - "language based image editing with recurrent attentive models"
    - "comprehension guided referring expressions"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-6 (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"
    - "photographic image synthesis with cascaded refinement networks"
    - "a neural representation of sketch drawings"
    - "adversarial nets with perceptual losses for text to image synthesis"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (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"
    - "adversarial nets with perceptual losses for text to image synthesis"
    - "learning what and where to draw"

Connected through hyperedge HE-8 (induced by source paper "text to image synthesis using generative adversarial networks"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"
    - "identity mappings in deep residual networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "least squares generative adversarial networks"

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)