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: "generating images part by part with composite generative adversarial networks"
  Abstract: Image generation remains a fundamental problem in artificial intelligence in general and deep learning in specific. The generative adversarial network (GAN) was successful in generating high quality samples of natural images. We propose a model called composite generative adversarial network, that reveals the complex structure of images with multiple generators in which each generator generates some part of the image. Those parts are combined by alpha blending process to create a new single image. It can generate, for example, background and face sequentially with two generators, after training on face dataset. Training was done in an unsupervised way without any labels about what each generator should generate. We found possibilities of learning the structure by using this generative model empirically.

Connected through hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "transformable bottleneck networks"
    - "generating multiple objects at spatially distinct locations"
    - "neural voxel renderer learning an accurate and controllable rendering tool"
    - "emergence of object segmentation in perturbed generative models"
    - "escaping plato s cave 3d shape from adversarial rendering"

Connected through hyperedge HE-2 (induced by source paper "generating object stamps"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "adaptive composition gan towards realistic image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "infinite brain mr images pggan based data augmentation for tumor detection"):
  with members:
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "brain tumor segmentation with deep neural networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "emergence of object segmentation in perturbed generative models"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "alphagan generative adversarial networks for natural image matting"
    - "learning to segment via cut and paste"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image matting"

Connected through hyperedge HE-5 (induced by source paper "investigating object compositionality in generative adversarial networks"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "emergence of object segmentation in perturbed generative models"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "unsupervised object segmentation by redrawing"
    - "object discovery with a copy pasting gan"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the gan landscape losses architectures regularization and normalization"

Connected through hyperedge HE-6 (induced by source paper "attention gan for object transfiguration in wild images"):
  with members:
    - "perceptual adversarial networks for image to image transformation"
    - "viton an image based virtual try on network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "one sided unsupervised domain mapping"
    - "photographic image synthesis with cascaded refinement networks"
    - "generative semantic manipulation with contrasting gan"

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"
    - "semi supervised fusedgan for conditional image generation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial image synthesis with decision tree latent controller"):
  with members:
    - "modulating early visual processing by language"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "improved training of wasserstein gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "stacked generative adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "generating images with recurrent 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.AI(Artificial Intelligence)