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: "stackgan text to photo realistic image synthesis with stacked generative adversarial networks"
  Abstract: Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing text-to-image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks recent developments"):
  with members:
    - "generative adversarial networks for extreme learned image compression"
    - "the gan landscape losses architectures regularization and normalization"
    - "improving gan training via binarized representation entropy bre regularization"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "large scale gan training for high fidelity natural image synthesis"
    - "point cloud gan"

Connected through hyperedge HE-2 (induced by source paper "justifying diagnosis decisions by deep neural networks"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "generating continuous representations of medical texts"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "realistic image generation using region phrase attention"):
  with members:
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning what and where to draw"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"):
  with members:
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "began boundary equilibrium generative adversarial networks"
    - "towards adversarial retinal image synthesis"
    - "sphereface deep hypersphere embedding for face recognition"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning deep convolutional embeddings for face representation using joint sample and set based supervision"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"

Connected through hyperedge HE-5 (induced by source paper "diy human action data set generation"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "progressive growing of gans for improved quality stability and variation"
    - "unsupervised image to image translation networks"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"
    - "transformation based models of video sequences"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-6 (induced by source paper "memorization precedes generation learning unsupervised gans with memory networks"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "face aging with conditional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "measuring catastrophic forgetting in neural networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "generative adversarial frontal view to bird view synthesis"):
  with members:
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "cross view image synthesis using conditional gans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep view morphing"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-8 (induced by source paper "canvasgan a simple baseline for text to image generation by incrementally patching a canvas"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "attention is all you need"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "toward multimodal image to image translation"
    - "learning deep representations of fine grained visual descriptions"

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)