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: "a semi supervised two stage approach to learning from noisy labels"
  Abstract: The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high.

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"
    - "unsupervised label noise modeling and loss correction"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "no regret sample selection with noisy labels"):
  with members:
    - "introduction to online convex optimization"
    - "nlnl negative learning for noisy labels"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint optimization framework for learning with noisy labels"
    - "not all samples are created equal deep learning with importance sampling"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-3 (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"
    - "unsupervised label noise modeling and loss correction"
    - "using pre training can improve model robustness and uncertainty"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "learning from synthetic animals"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "confidence regularized self training"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "a robust learning approach to domain adaptive object detection"
    - "nlnl negative learning for noisy labels"
    - "automatic adaptation of object detectors to new domains using self training"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"

Connected through hyperedge HE-5 (induced by source paper "distilling effective supervision from severe label noise"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "randaugment practical data augmentation with no separate search"
    - "nlnl negative learning for noisy labels"
    - "unsupervised label noise modeling and loss correction"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep self learning from noisy labels"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "joint optimization framework for learning with noisy labels"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"

Connected through hyperedge HE-7 (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"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-8 (induced by source paper "the resistance to label noise in k nn and cnn depends on its concentration"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "the devil of face recognition is in the noise"
    - "deep learning is robust to massive label noise"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for 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"

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)