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: "unsupervised label noise modeling and loss correction"
  Abstract: Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "dividemix learning with noisy labels as semi supervised learning"):
  with members:
    - "robust learning from untrusted sources"
    - "how does disagreement help generalization against label corruption"
    - "learning from noisy labels by regularized estimation of annotator confusion"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "towards noise resistant object detection with noisy annotations"):
  with members:
    - "how does disagreement help generalization against label corruption"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "training object detectors with noisy data"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "identifying mislabeled data using the area under the margin ranking"):
  with members:
    - "dividemix learning with noisy labels as semi supervised learning"
    - "convolutional networks with dense connectivity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "towards robust learning with different label noise distributions"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "deep self learning from noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "where is the information in a deep neural network"
    - "how does disagreement help generalization against label corruption"
    - "meta learning without memorization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "measuring the intrinsic dimension of objective landscapes"

Connected through hyperedge HE-5 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "deep self learning from noisy labels"
    - "billion scale semi supervised learning for image classification"
    - "are anchor points really indispensable in label noise learning"
    - "an effective label noise model for dnn text classification"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-6 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-7 (induced by source paper "towards robust learning with different label noise distributions"):
  with members:
    - "deep self learning from noisy labels"
    - "are anchor points really indispensable in label noise learning"
    - "accelerating deep learning by focusing on the biggest losers"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "nlnl negative learning for noisy labels"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"
    - "understanding and utilizing deep neural networks trained with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "countering noisy labels by learning from auxiliary clean labels"):
  with members:
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "group normalization"
    - "there are many consistent explanations of unlabeled data why you should average"

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)