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: "pixelcnn models with auxiliary variables for natural image modeling"
  Abstract: We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models.

Connected through hyperedge HE-1 (induced by source paper "hierarchical auto regressive model for image compression incorporating object saliency and a deep perceptual loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"
    - "conditional probability models for deep image compression"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-3 (induced by source paper "generating diverse high fidelity images with vq vae 2"):
  with members:
    - "classification accuracy score for conditional generative models"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "natural image manipulation for autoregressive models using fisher scores"):
  with members:
    - "large scale adversarial representation learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "the challenge of realistic music generation modelling raw audio at scale"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "associative compression networks for representation learning"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dilated recurrent neural networks"
    - "learning a hierarchical latent variable model of 3d shapes"

Connected through hyperedge HE-6 (induced by source paper "probabilistic semantic inpainting with pixel constrained cnns"):
  with members:
    - "attentive neural processes"
    - "image inpainting for irregular holes using partial convolutions"
    - "neural processes"
    - "free form image inpainting with gated convolution"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "generative image inpainting with contextual attention"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-7 (induced by source paper "on gans and gmms"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"

Connected through hyperedge HE-8 (induced by source paper "analyzing inverse problems with invertible neural networks"):
  with members:
    - "neural autoregressive flows"
    - "training generative reversible networks"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"

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)