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 "wasserstein adversarial regularization war on label noise"):
  Abstract: Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance. Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space. Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art competitors.

Includes members:
  Member "symmetric cross entropy for robust learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from web data with self organizing memory module")
  Member "label smoothing and logit squeezing a replacement for adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cat customized adversarial training for improved robustness")
  Member "how does disagreement help generalization against label corruption":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "adversarial robustness via adversarial label smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cat customized adversarial training for improved robustness")
  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 "the devil of face recognition is in the noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "joint optimization framework for learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "dimensionality driven learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")

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.LG(Machine Learning)