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: "semi supervised fusedgan for conditional image generation"
  Abstract: We present FusedGAN, a deep network for conditional image synthesis with controllable sampling of diverse images. Fidelity, diversity and controllable sampling are the main quality measures of a good image generation model. Most existing models are insufficient in all three aspects. The FusedGAN can perform controllable sampling of diverse images with very high fidelity. We argue that controllability can be achieved by disentangling the generation process into various stages. In contrast to stacked GANs, where multiple stages of GANs are trained separately with full supervision of labeled intermediate images, the FusedGAN has a single stage pipeline with a built-in stacking of GANs. Unlike existing methods, which requires full supervision with paired conditions and images, the FusedGAN can effectively leverage more abundant images without corresponding conditions in training, to produce more diverse samples with high fidelity. We achieve this by fusing two generators: one for unconditional image generation, and the other for conditional image generation, where the two partly share a common latent space thereby disentangling the generation. We demonstrate the efficacy of the FusedGAN in fine grained image generation tasks such as text-to-image, and attribute-to-face generation.

Connected through hyperedge HE-1 (induced by source paper "hierarchical modes exploring in generative adversarial networks"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "on gans and gmms"
    - "stochastic adversarial video prediction"
    - "on line adaptative curriculum learning for gans"
    - "visual data synthesis via gan for zero shot video classification"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-2 (induced by source paper "adversarial colorization of icons based on structure and color conditions"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "conditional image to image translation"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-3 (induced by source paper "dualdis dual branch disentangling with adversarial learning"):
  with members:
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "dual swap disentangling"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "fully featured attribute transfer"):
  with members:
    - "modular generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "layoutvae stochastic scene layout generation from a label set"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "imagine this scripts to compositions to videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "grains generative recursive autoencoders for indoor scenes"

Connected through hyperedge HE-6 (induced by source paper "glyphgan style consistent font generation based on generative adversarial networks"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "chinese typography transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"
    - "aligngan learning to align cross domain images with conditional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"):
  with members:
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unsupervised learning of disentangled representations from video"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "fast converging conditional generative adversarial networks for image synthesis"):
  with members:
    - "age progression regression by conditional adversarial autoencoder"
    - "neural photo editing with introspective adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "learning what and where to draw"
    - "improved techniques for training gans"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"
    - "pixel recurrent neural 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)