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 "mean shift rejection training deep neural networks without minibatch statistics or normalization"):
  Abstract: Deep convolutional neural networks are known to be unstable during training at high learning rate unless normalization techniques are employed. Normalizing weights or activations allows the use of higher learning rates, resulting in faster convergence and higher test accuracy. Batch normalization requires minibatch statistics that approximate the dataset statistics but this incurs additional compute and memory costs and causes a communication bottleneck for distributed training. Weight normalization and initialization-only schemes do not achieve comparable test accuracy. #R##N#We introduce a new understanding of the cause of training instability and provide a technique that is independent of normalization and minibatch statistics. Our approach treats training instability as a spatial common mode signal which is suppressed by placing the model on a channel-wise zero-mean isocline that is maintained throughout training. Firstly, we apply channel-wise zero-mean initialization of filter kernels with overall unity kernel magnitude. At each training step we modify the gradients of spatial kernels so that their weighted channel-wise mean is subtracted in order to maintain the common mode rejection condition. This prevents the onset of mean shift. This new technique allows direct training of the test graph so that training and test models are identical. We also demonstrate that injecting random noise throughout the network during training improves generalization. This is based on the idea that, as a side effect, batch normalization performs deep data augmentation by injecting minibatch noise due to the weakness of the dataset approximation. #R##N#Our technique achieves higher accuracy compared to batch normalization and for the first time shows that minibatches and normalization are unnecessary for state-of-the-art training.

Includes members:
  Member "filter response normalization layer eliminating batch dependence in the training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "fixup initialization residual learning without normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking batch normalization in transformers")
  Member "generalized batch normalization towards accelerating deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extended batch normalization")
  Member "deep residual networks and weight initialization":
    also connected through other hyperedges:
      (none)
  Member "comparison of batch normalization and weight normalization algorithms for the large scale image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "weight normalization a simple reparameterization to accelerate training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "recurrent batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking batch normalization in transformers")

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)