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: "critical learning periods in deep neural networks"
  Abstract: Critical periods are phases in the early development of humans and animals during which experience can affect the structure of neuronal networks irreversibly. In this work, we study the effects of visual stimulus deficits on the training of artificial neural networks (ANNs). Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural network causes irreversible performance loss that closely mimics that reported in humans and animal models. Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANN's performance and can be rapidly overcome with additional training, as observed in humans. In addition, deeper networks show a more prominent critical period. To better understand this phenomenon, we use techniques from information theory to study the strength of the network connections during training. Our analysis suggests that the first few epochs are critical for the allocation of resources across different layers, determined by the initial input data distribution. Once such information organization is established, the network resources do not re-distribute through additional training. These findings suggest that the initial rapid learning phase of training of ANNs, under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks.

Connected through hyperedge HE-1 (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"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "triple memory networks a brain inspired method for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-3 (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"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a new mram based process in memory accelerator for efficient neural network training with floating point precision"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "imagenet training in minutes"
    - "computing in memory with spin transfer torque magnetic ram"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"

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"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deepcabac a universal compression algorithm for deep neural networks"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "scalable methods for 8 bit training of neural networks"
    - "compact and computationally efficient representation of deep neural networks"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "training deep neural networks with 8 bit floating point numbers"
    - "learning sparse neural networks via sensitivity driven regularization"
    - "universal deep neural network compression"

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)