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 "comparison of batch normalization and weight normalization algorithms for the large scale image classification"):
  Abstract: Batch normalization (BN) has become a de facto standard for training deep convolutional networks. However, BN accounts for a significant fraction of training run-time and is difficult to accelerate, since it is a memory-bandwidth bounded operation. Such a drawback of BN motivates us to explore recently proposed weight normalization algorithms (WN algorithms), i.e. weight normalization, normalization propagation and weight normalization with translated ReLU. These algorithms don't slow-down training iterations and were experimentally shown to outperform BN on relatively small networks and datasets. However, it is not clear if these algorithms could replace BN in practical, large-scale applications. We answer this question by providing a detailed comparison of BN and WN algorithms using ResNet-50 network trained on ImageNet. We found that although WN achieves better training accuracy, the final test accuracy is significantly lower ($\approx 6\%$) than that of BN. This result demonstrates the surprising strength of the BN regularization effect which we were unable to compensate for using standard regularization techniques like dropout and weight decay. We also found that training of deep networks with WN algorithms is significantly less stable compared to BN, limiting their practical applications.

Includes members:
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "instance normalization the missing ingredient for fast stylization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "wide residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "weight normalization a simple reparameterization to accelerate training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")

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.CV(Computer Vision and Pattern Recognition)