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 "identifying mislabeled data using the area under the margin ranking"):
  Abstract: Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled. This paper introduces a new method to identify such samples and mitigate their impact when training neural networks. At the heart of our algorithm is the Area Under the Margin (AUM) statistic, which exploits differences in the training dynamics of clean and mislabeled samples. A simple procedure - adding an extra class populated with purposefully mislabeled indicator samples - learns a threshold that isolates mislabeled data based on this metric. This approach consistently improves upon prior work on synthetic and real-world datasets. On the WebVision50 classification task our method removes 17% of training data, yielding a 2.6% (absolute) improvement in test error. On CIFAR100 removing 13% of the data leads to a 1.2% drop in error.

Includes members:
  Member "dividemix learning with noisy labels as semi supervised learning":
    also connected through other hyperedges:
      (none)
  Member "convolutional networks with dense connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on feature normalization and data augmentation")
  Member "simple and effective regularization methods for training on noisily labeled data with generalization guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving generalization by controlling label noise information in neural network weights")
  Member "towards robust learning with different label noise distributions":
    also connected through other hyperedges:
      (none)
  Member "self learning to filter noisy labels with self ensembling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self adaptive training beyond empirical risk minimization")
  Member "image classification with deep learning in the presence of noisy labels a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "unsupervised label noise modeling and loss correction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "l_dmi an information theoretic noise robust loss function":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning adaptive loss for robust learning with noisy labels")

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)