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: "learning deep networks from noisy labels with dropout regularization"
  Abstract: Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is overdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "image recognition from raw labels collected without annotators"):
  with members:
    - "a simple yet effective baseline for robust deep learning with noisy labels"
    - "synthetic vs real deep learning on controlled noise"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "limited gradient descent learning with noisy labels"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "deep decoder concise image representations from untrained non convolutional networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "nlnl negative learning for noisy labels"):
  with members:
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"

Connected through hyperedge HE-4 (induced by source paper "an effective label noise model for dnn text classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "masking a new perspective of noisy supervision"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "recent trends in deep learning based natural language processing"
    - "deep learning from noisy image labels with quality embedding"
    - "toward robustness against label noise in training deep discriminative neural networks"

Connected through hyperedge HE-5 (induced by source paper "robust deep graph based learning for binary classification"):
  with members:
    - "deep learning is robust to massive label noise"
    - "graph laplacian regularized graph convolutional networks for semi supervised learning"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "dimensionality driven learning with noisy labels"
    - "dynamic graph cnn for learning on point clouds"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "learning to reweight examples for robust deep learning"
    - "iterative learning with open set noisy labels"

Connected through hyperedge HE-6 (induced by source paper "leveraging inductive bias of neural networks for learning without explicit human annotations"):
  with members:
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "limited gradient descent learning with noisy labels"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-7 (induced by source paper "learning with noisy labels for sentence level sentiment classification"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "masking a new perspective of noisy supervision"
    - "decoupling when to update from how to update"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "learning from noisy large scale datasets with minimal 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"
    - "label noise robust generative adversarial networks"
    - "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"

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)