human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks"):
  with members:
    - "a mean field theory of batch normalization"
    - "fixup initialization residual learning without normalization"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "weight standardization"
    - "a quantization friendly separable convolution for mobilenets"
    - "group normalization"
    - "spectrum concentration in deep residual learning a free probability approach"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "understanding the disharmony between weight normalization family and weight decay e shifted l 2 regularizer"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "weight standardization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mixed link networks"
    - "differentiable learning to normalize via switchable normalization"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "accelerating training of deep neural networks with a standardization loss"):
  with members:
    - "group normalization"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "progressive growing of gans for improved quality stability and variation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-4 (induced by source paper "mean spectral normalization of deep neural networks for embedded automation"):
  with members:
    - "deep learning algorithms for bearing fault diagnosticsx a comprehensive review"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "understanding batch normalization"
    - "generative adversarial network based autoencoder application to fault detection problem for closed loop dynamical systems"
    - "generative adversarial learning for spectrum sensing"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "online normalization for training neural networks"):
  with members:
    - "mode normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "l2 regularization versus batch and weight normalization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "instance normalization the missing ingredient for fast stylization"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-6 (induced by source paper "sparsetrain leveraging dynamic sparsity in training dnns on general purpose simd processors"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "efficient sparse winograd convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "sparce sparsity aware general purpose core extensions to accelerate deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "equi normalization of neural networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "instance normalization the missing ingredient for fast stylization"
    - "practical riemannian neural networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "all you need is a good init"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"

Connected through hyperedge HE-8 (induced by source paper "a mean field theory of batch normalization"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "the nonlinearity coefficient predicting overfitting in deep neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Question: Please tell me which class the center node 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)