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: "label noise robust generative adversarial networks"
  Abstract: Generative adversarial networks (GANs) are a framework that learns a generative distribution through adversarial training. Recently, their class-conditional extensions (e.g., conditional GAN (cGAN) and auxiliary classifier GAN (AC-GAN)) have attracted much attention owing to their ability to learn the disentangled representations and to improve the training stability. However, their training requires the availability of large-scale accurate class-labeled data, which are often laborious or impractical to collect in a real-world scenario. To remedy this, we propose a novel family of GANs called label-noise robust GANs (rGANs), which, by incorporating a noise transition model, can learn a clean label conditional generative distribution even when training labels are noisy. In particular, we propose two variants: rAC-GAN, which is a bridging model between AC-GAN and the label-noise robust classification model, and rcGAN, which is an extension of cGAN and solves this problem with no reliance on any classifier. In addition to providing the theoretical background, we demonstrate the effectiveness of our models through extensive experiments using diverse GAN configurations, various noise settings, and multiple evaluation metrics (in which we tested 402 conditions in total). Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-2 (induced by source paper "multi class classification from noisy similarity labeled data"):
  with members:
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "are anchor points really indispensable in label noise learning"
    - "classification from pairwise similarities dissimilarities and unlabeled data via empirical risk minimization"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "noise tolerant paradigm for training face recognition cnns"
    - "multi class classification without multi class labels"

Connected through hyperedge HE-3 (induced by source paper "noise robust generative adversarial networks"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "self supervised gans via auxiliary rotation loss"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-4 (induced by source paper "towards visually explaining variational autoencoders"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "sliced wasserstein generative models"
    - "deep learning for anomaly detection a survey"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "re identification with consistent attentive siamese networks"
    - "sharpen focus learning with attention separability and consistency"

Connected through hyperedge HE-5 (induced by source paper "generative discriminative complementary learning"):
  with members:
    - "twin auxiliary classifiers gan"
    - "cgans with projection discriminator"
    - "attention based deep multiple instance learning"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "good semi supervised learning that requires a bad gan"
    - "positive unlabeled learning with non negative risk estimator"

Connected through hyperedge HE-6 (induced by source paper "label noise robust multi domain image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "deep learning is robust to massive label noise"
    - "smit stochastic multi label image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "on self modulation for generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "disentanglement based active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "generative adversarial network in medical imaging a review"
    - "how good is my gan"
    - "generative adversarial active learning"
    - "structured generative adversarial networks"
    - "triple generative adversarial nets"
    - "guiding infogan with semi supervision"

Connected through hyperedge HE-8 (induced by source paper "lead2gold towards exploiting the full potential of noisy transcriptions for speech recognition"):
  with members:
    - "active and semi supervised learning in asr benefits on the acoustic and language models"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "a fully differentiable beam search decoder"
    - "wav2letter the fastest open source speech recognition system"
    - "letter based speech recognition with gated convnets"
    - "improving lstm ctc based asr performance in domains with limited training data"
    - "wav2letter an end to end convnet based speech recognition system"
    - "learning deep networks from noisy labels with dropout regularization"

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)