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: "megapixel size image creation using generative adversarial networks"
  Abstract: Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.

Connected through hyperedge HE-1 (induced by source paper "earthquakegen earthquake simulation using generative adversarial networks"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "gansynth adversarial neural audio synthesis"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-2 (induced by source paper "scenic a language for scenario specification and scene generation"):
  with members:
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "recurrent topic transition gan for visual paragraph generation"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"
    - "improved relation classification by deep recurrent neural networks with data augmentation"

Connected through hyperedge HE-3 (induced by source paper "loganv2 conditional style based logo generation with generative adversarial networks"):
  with members:
    - "logan generating logos with a generative adversarial neural network conditioned on color"
    - "a style based generator architecture for generative adversarial networks"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "logo synthesis and manipulation with clustered generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "large scale gan training for high fidelity natural image synthesis"):
  with members:
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "comparing generative adversarial network techniques for image creation and modification"
    - "improving gans using optimal transport"
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "which training methods for gans do actually converge"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-5 (induced by source paper "a style based generator architecture for generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "improved training with curriculum gans"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "counterexample guided data augmentation"):
  with members:
    - "semantic adversarial deep learning"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "recurrent topic transition gan for visual paragraph generation"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "training region based object detectors with online hard example mining"
    - "a theory of formal synthesis via inductive learning"

Connected through hyperedge HE-7 (induced by source paper "high resolution medical image synthesis using progressively grown generative adversarial networks"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "medical image synthesis with context aware generative adversarial networks"
    - "hemis hetero modal image segmentation"
    - "adversarial feature learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "progressive growing of gans for improved quality stability and variation"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "pacgan the power of two samples in generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "l2 regularization versus batch and weight normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"

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)