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: "towards robust learning with different label noise distributions"
  Abstract: Noisy labels are an unavoidable consequence of automatic image labeling processes to reduce human supervision. Training in these conditions leads Convolutional Neural Networks to memorize label noise and degrade performance. Noisy labels are therefore dispensable, while image content can be exploited in a semi-supervised learning (SSL) setup. Handling label noise then becomes a label noise detection task. Noisy/clean samples are usually identified using the \textit{small loss trick}, which is based on the observation that clean samples represent easier patterns and, therefore, exhibit a lower loss. However, we show that different noise distributions make the application of this trick less straightforward. We propose to continuously relabel all images to reveal a loss that facilitates the use of the small loss trick with different noise distributions. SSL is then applied twice, once to improve the clean-noisy detection and again for training the final model. We design an experimental setup for better understanding the consequences of differing label noise distributions and find that non-uniform out-of-distribution noise better resembles real-world noise. We show that SSL outperforms other alternatives when using oracles and demonstrate substantial improvements across five datasets of our label noise Distribution Robust Pseudo-Labeling (DRPL). We further study the effects of label noise memorization via linear probes and find that in most cases intermediate features are not affected by label noise corruption. Code and details to reproduce our framework will be made available.

Connected through hyperedge HE-1 (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"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "unsupervised label noise modeling and loss correction"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

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)