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 "improving generalization by controlling label noise information in neural network weights"):
  Abstract: In the presence of noisy or incorrect labels, neural networks have the undesirable tendency to memorize information about the noise. Standard regularization techniques such as dropout, weight decay or data augmentation sometimes help, but do not prevent this behavior. If one considers neural network weights as random variables that depend on the data and stochasticity of training, the amount of memorized information can be quantified with the Shannon mutual information between weights and the vector of all training labels given inputs, $I(w : \mathbf{y} \mid \mathbf{x})$. We show that for any training algorithm, low values of this term correspond to reduction in memorization of label-noise and better generalization bounds. To obtain these low values, we propose training algorithms that employ an auxiliary network that predicts gradients in the final layers of a classifier without accessing labels. We illustrate the effectiveness of our approach on versions of MNIST, CIFAR-10, and CIFAR-100 corrupted with various noise models, and on a large-scale dataset Clothing1M that has noisy labels.

Includes members:
  Member "deep self learning from noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  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 "identifying mislabeled data using the area under the margin ranking")
  Member "where is the information in a deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations")
  Member "how does disagreement help generalization against label corruption":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  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 "meta learning without memorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental object detection via meta learning")
  Member "understanding and utilizing deep neural networks trained with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "meta weight net learning an explicit mapping for sample weighting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep 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)