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: "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
  Abstract: Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges. Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, even if the final loss landscape is the same as if there had been no regularization. In some cases, generalization even improves after interrupting regularization. Conversely, if regularization is applied only after the initial transient, it has no effect on the final solution, whose generalization gap is as bad as if regularization never happened. This suggests that what matters for training deep networks is not just whether or how, but when to regularize. The phenomena we observe are manifest in different datasets (CIFAR-10, CIFAR-100), different architectures (ResNet-18, All-CNN), different regularization methods (weight decay, data augmentation), different learning rate schedules (exponential, piece-wise constant). They collectively suggest that there is a ``critical period'' for regularizing deep networks that is decisive of the final performance. More analysis should, therefore, focus on the transient rather than asymptotic behavior of learning.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"

Connected through hyperedge HE-2 (induced by source paper "the break even point on optimization trajectories of deep neural networks"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "lca loss change allocation for neural network training"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "the local elasticity of neural networks"
    - "emergent properties of the local geometry of neural loss landscapes"

Connected through hyperedge HE-3 (induced by source paper "population based training for loss function optimization"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "visualising basins of attraction for the cross entropy and the squared error neural network loss functions"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "evolved policy gradients"

Connected through hyperedge HE-4 (induced by source paper "wide minima density hypothesis and the explore exploit learning rate schedule"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "towards understanding generalization in gradient based meta learning"
    - "an empirical model of large batch training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-5 (induced by source paper "the early phase of neural network training"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "stabilizing the lottery ticket hypothesis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "unsupervised representation learning by predicting image rotations"
    - "gradient descent happens in a tiny subspace"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-6 (induced by source paper "evolving loss functions with multivariate taylor polynomial parameterizations"):
  with members:
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "2 5d visual sound"
    - "evolving deep neural networks"
    - "semantic image synthesis via adversarial learning"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "tensorflow a system for large scale machine learning"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "data augmentation revisited rethinking the distribution gap between clean and augmented data"):
  with members:
    - "implicit semantic data augmentation for deep networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "fast autoaugment"
    - "bag of tricks for image classification with convolutional neural networks"
    - "a kernel theory of modern data augmentation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "finding the needle in the haystack with convolutions on the benefits of architectural bias"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "shaping the learning landscape in neural networks around wide flat minima"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "three factors influencing minima in sgd"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

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.LG(Machine Learning)