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: "deep learning from noisy image labels with quality embedding"
  Abstract: There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a quality embedding model, which explicitly introduces a quality variable to represent the trustworthiness of noisy labels. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the noise effect. At the same time, the high-quality labels is still able to be applied for training. To instantiate the model, we further propose a Contrastive-Additive Noise network (CAN), which consists of two important layers: (1) the contrastive layer estimates the quality variable in the embedding space to reduce noise effect; and (2) the additive layer aggregates the prior predictions and noisy labels as the posterior to train the classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We conduct the experimental evaluation of the proposed method over a range of noisy image datasets. Comprehensive results have demonstrated CAN outperforms the state-of-the-art deep learning approaches.

Connected through hyperedge HE-1 (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"
    - "learning deep networks from noisy labels with dropout regularization"
    - "toward robustness against label noise in training deep discriminative neural networks"

Connected through hyperedge HE-2 (induced by source paper "uncertainty based detection and relabeling of noisy image labels"):
  with members:
    - "unsupervised label noise modeling and loss correction"
    - "deep bilevel learning"
    - "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-3 (induced by source paper "safeguarded dynamic label regression for generalized noisy supervision"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "learning to reweight examples for robust deep learning"
    - "masking a new perspective of noisy supervision"
    - "domain adaptation for visual applications a comprehensive survey"
    - "mixup beyond empirical risk minimization"
    - "fast mixing for latent dirichlet allocation"
    - "webvision database visual learning and understanding from web data"
    - "learning from noisy labels with distillation"

Connected through hyperedge HE-4 (induced by source paper "probabilistic end to end noise correction for learning with noisy labels"):
  with members:
    - "deep learning is robust to massive label noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "iterative learning with open set noisy labels"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep label distribution learning with label ambiguity"
    - "cleannet transfer learning for scalable image classifier training with label noise"

Connected through hyperedge HE-5 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "learning from weak and noisy labels for semantic segmentation"
    - "learning deep networks from noisy labels with dropout regularization"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "deep residual learning for image recognition"
    - "discovering attribute shades of meaning with the crowd"

Connected through hyperedge HE-6 (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)