human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "countering noisy labels by learning from auxiliary clean labels"):
  Abstract: We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we also consider the pseudo labels in semi-supervised learning (Semi-SL) as a special case of NL. For both types of noise, we argue that the generalization performance of existing methods is highly coupled with the quality of noisy labels. Therefore, we counter the problem from a novel and unified perspective: learning from the auxiliary clean labels. Specifically, we propose the Rotational-Decoupling Consistency Regularization (RDCR) framework that integrates the consistency-based methods with the self-supervised rotation task to learn noise-tolerant representations. The experiments show that RDCR achieves comparable or superior performance than the state-of-the-art methods under small noise, while outperforms the existing methods significantly when there is large noise.

Includes members:
  Member "a meta approach to defend noisy labels by the manifold regularizer psdr":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition")
  Member "unsupervised label noise modeling and loss correction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "pushing the right boundaries matters wasserstein adversarial training for label noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition")
  Member "weight standardization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking batch normalization in transformers")
  Member "understanding and utilizing deep neural networks trained with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "revisiting self supervised visual representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning representations by predicting bags of visual words")
  Member "high fidelity image generation with fewer labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "group normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

Question: Please tell me which class the source paper 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)