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: "decorrelated batch normalization"
  Abstract: Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.

Connected through hyperedge HE-1 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "policy optimization as wasserstein gradient flows"
    - "understanding batch normalization"
    - "measuring and regularizing networks in function space"
    - "mode normalization"

Connected through hyperedge HE-2 (induced by source paper "an internal covariate shift bounding algorithm for deep neural networks by unitizing layers outputs"):
  with members:
    - "group normalization"
    - "riemannian approach to batch normalization"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"
    - "mask r cnn"
    - "instance normalization the missing ingredient for fast stylization"
    - "identity mappings in deep residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "improving stdp based visual feature learning with whitening"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"

Connected through hyperedge HE-4 (induced by source paper "four things everyone should know to improve batch normalization"):
  with members:
    - "truth or backpropaganda an empirical investigation of deep learning theory"
    - "fixup initialization residual learning without normalization"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain adaptation using feature whitening and consensus loss"):
  with members:
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "boosting domain adaptation by discovering latent domains"
    - "kitting in the wild through online domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "iterative normalization beyond standardization towards efficient whitening"):
  with members:
    - "differentiable learning to normalize via switchable normalization"
    - "normalization of neural networks using analytic variance propagation"
    - "understanding batch normalization"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "improved bilinear pooling with cnns"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-7 (induced by source paper "accelerating training of deep neural networks with a standardization loss"):
  with members:
    - "group normalization"
    - "understanding 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"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-8 (induced by source paper "approximated orthonormal normalisation in training neural networks"):
  with members:
    - "orthogonal deep neural networks"
    - "on the convergence of adam and beyond"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

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)