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: "how good is my gan"
  Abstract: Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.

Connected through hyperedge HE-1 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"
    - "modulating early visual processing by language"

Connected through hyperedge HE-2 (induced by source paper "generative pseudo label refinement for unsupervised domain adaptation"):
  with members:
    - "deep learning is robust to massive label noise"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "realistic river image synthesis using deep generative adversarial networks"):
  with members:
    - "decentralized flood forecasting using deep neural networks"
    - "a style based generator architecture for generative adversarial networks"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "generative modeling using the sliced wasserstein distance"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial generation of training examples applications to moving vehicle license plate recognition"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "improving the evaluation of generative models with fuzzy logic"):
  with members:
    - "geometry score a method for comparing generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improved techniques for training gans"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "dreaming to distill data free knowledge transfer via deepinversion"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "relational knowledge distillation"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "variational information distillation for knowledge transfer"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"

Connected through hyperedge HE-6 (induced by source paper "the six fronts of the generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "skin lesion synthesis with generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "sdit scalable and diverse cross domain image translation"):
  with members:
    - "controlling biases and diversity in diverse image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-8 (induced by source paper "image synthesis with a single robust classifier"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

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)