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 "learning not to learn in the presence of noisy labels"):
  Abstract: Learning in the presence of label noise is a challenging yet important task: it is crucial to design models that are robust in the presence of mislabeled datasets. In this paper, we discover that a new class of loss functions called the gambler's loss provides strong robustness to label noise across various levels of corruption. We show that training with this loss function encourages the model to "abstain" from learning on the data points with noisy labels, resulting in a simple and effective method to improve robustness and generalization. In addition, we propose two practical extensions of the method: 1) an analytical early stopping criterion to approximately stop training before the memorization of noisy labels, as well as 2) a heuristic for setting hyperparameters which do not require knowledge of the noise corruption rate. We demonstrate the effectiveness of our method by achieving strong results across three image and text classification tasks as compared to existing baselines.

Includes members:
  Member "laprop a better way to combine momentum with adaptive gradient":
    also connected through other hyperedges:
      (none)
  Member "deep gamblers learning to abstain with portfolio theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial robustness for code")
  Member "sgd on neural networks learns functions of increasing complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we need zero training loss after achieving zero training error")
  Member "simple and effective regularization methods for training on noisily labeled data with generalization guarantee":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying mislabeled data using the area under the margin ranking")
  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 "selectivenet a deep neural network with an integrated reject option":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "l_dmi an information theoretic noise robust loss function":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying mislabeled data using the area under the margin ranking")
  Member "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identifying mislabeled data using the area under the margin ranking")

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)