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 "leveraging inductive bias of neural networks for learning without explicit human annotations"):
  Abstract: Classification problems today are often solved by first collecting examples along with candidate labels, second obtaining clean labels from workers, and third training a large, overparameterized deep neural network on the clean examples. The second, manual labeling step is often the most expensive one as it requires manually going through all examples. In this paper we propose to i) skip the manual labeling step entirely, ii) directly train the deep neural network on the noisy candidate labels, and iii) early stop the training to avoid overfitting. With this procedure we exploit an intriguing property of large overparameterized neural networks: While they are capable of perfectly fitting the noisy data, gradient descent fits clean labels faster than noisy ones. Thus, training and early stopping on noisy labels resembles training on clean labels only. Our results show that early stopping the training of standard deep networks (such as ResNet-18) on a subset of the Tiny Images dataset (which is obtained without any explicit human labels and only about half of the labels are correct), gives a significantly higher test performance than when trained on the clean CIFAR-10 training dataset (which is obtained by labeling a subset of the Tiny Images dataset). In addition, our results show that the noise generated through the label collection process is not nearly as adversarial for learning as the noise generated by randomly flipping labels, which is the noise most prevalent in works demonstrating noise robustness of neural networks.

Includes members:
  Member "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "identifying mislabeled data using the area under the margin ranking")
  Member "fine grained analysis of optimization and generalization for overparameterized two layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "limited gradient descent learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image recognition from raw labels collected without annotators")
  Member "deep learning is robust to massive label noise":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "derivative manipulation for general example weighting")
  Member "learning with bad training data via iterative trimmed loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "joint optimization framework for learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "learning to reweight examples for robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise estimation using density estimation for self supervised multimodal 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)