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: "adversarial generation of training examples applications to moving vehicle license plate recognition"
  Abstract: Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5% over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.

Connected through hyperedge HE-1 (induced by source paper "realistic river image synthesis using deep generative adversarial networks"):
  with members:
    - "decentralized flood forecasting using deep neural networks"
    - "how good is my gan"
    - "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"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "dada deep adversarial data augmentation for extremely low data regime classification"):
  with members:
    - "good semi supervised learning that requires a bad gan"
    - "a classification based study of covariate shift in gan distributions"
    - "lifelong learning crf for supervised aspect extraction"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "do gans actually learn the distribution an empirical study"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-3 (induced by source paper "parametric synthesis of text on stylized backgrounds using pggans"):
  with members:
    - "design design inspiration from generative networks"
    - "neural signatures for licence plate re identification"
    - "order embeddings and character level convolutions for multimodal alignment"
    - "it takes only two adversarial generator encoder networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "be your own prada fashion synthesis with structural coherence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "how many labeled license plates are needed"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "learning from simulated and unsupervised images through adversarial training"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "synthetic data for text localisation in natural images"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-5 (induced by source paper "artificial generation of big data for improving image classification a generative adversarial network approach on sar data"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "deep residual learning for image recognition"

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)