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: "image transformer"
  Abstract: Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. While conceptually simple, our generative models significantly outperform the current state of the art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet from 3.83 to 3.77. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we find that images generated by our super-resolution model fool human observers three times more often than the previous state of the art.

Connected through hyperedge HE-1 (induced by source paper "adversarial code learning for image generation"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "polygen an autoregressive generative model of 3d meshes"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "the curious case of neural text degeneration"
    - "stabilizing transformers for reinforcement learning"
    - "generating long sequences with sparse transformers"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "pointgrow autoregressively learned point cloud generation with self attention"

Connected through hyperedge HE-3 (induced by source paper "closing the dequantization gap pixelcnn as a single layer flow"):
  with members:
    - "learning discrete distributions by dequantization"
    - "integer discrete flows and lossless compression"
    - "sum of squares polynomial flow"
    - "discrete flows invertible generative models of discrete data"
    - "generating long sequences with sparse transformers"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-4 (induced by source paper "the impact of hole geometry on relative robustness of in painting networks an empirical study"):
  with members:
    - "foreground aware image inpainting"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "free form image inpainting with gated convolution"
    - "partial convolution based padding"
    - "generative image inpainting with contextual attention"
    - "segan segmenting and generating the invisible"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-5 (induced by source paper "sketchformer transformer based representation for sketched structure"):
  with members:
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "faster transformer decoding n gram masked self attention"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "fast transformer decoding one write head is all you need"
    - "generating wikipedia by summarizing long sequences"
    - "accelerating neural transformer via an average attention network"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "attention augmented convolutional networks"):
  with members:
    - "the evolved transformer"
    - "bam bottleneck attention module"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "self attention with relative position representations"
    - "backprop evolution"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-8 (induced by source paper "context gated convolution"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "attention augmented convolutional networks"
    - "generating long sequences with sparse transformers"
    - "weight standardization"
    - "eco efficient convolutional network for online video understanding"
    - "bam bottleneck attention module"

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)