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: "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
  Abstract: In this paper we study how neural network architecture affects the speed of training. We introduce a simple concept called gradient confusion to help formally analyze this. When confusion is high, stochastic gradients produced by different data samples may be negatively correlated, slowing down convergence. But when gradient confusion is low, data samples interact harmoniously, and training proceeds quickly. Through novel theoretical and experimental results, we show how the neural net architecture affects gradient confusion, and thus the efficiency of training. We show that for popular initialization techniques used in deep learning, increasing the width of neural networks leads to lower gradient confusion, and thus easier model training. On the other hand, increasing the depth of neural networks has the opposite effect. Further, when using orthogonal initialization, we show that the training dynamics early on become independent of the depth for linear neural networks, suggesting a way forward for training deep models. Finally, we observe that the combination of batch normalization and skip connections reduces gradient confusion, which helps reduce the training burden of very deep networks with Gaussian initializations.

Connected through hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "a mean field theory of batch normalization"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "circuit based intrinsic methods to detect overfitting"
    - "deep learning is robust to massive label noise"
    - "the global optimization geometry of shallow linear neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-3 (induced by source paper "explaining memorization and generalization a large scale study with coherent gradients"):
  with members:
    - "coherent gradients an approach to understanding generalization in gradient descent based optimization"
    - "stiffness a new perspective on generalization in neural networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "expandnets linear over parameterization to train compact convolutional networks"):
  with members:
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning deep representations with probabilistic knowledge transfer"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-5 (induced by source paper "exploiting operation importance for differentiable neural architecture search"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "searching for a robust neural architecture in four gpu hours"
    - "searching for mobilenetv3"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "multinomial distribution learning for effective neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "stacnas towards stable and consistent differentiable neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "understanding and robustifying differentiable architecture search"
    - "xnas neural architecture search with expert advice"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "darts improved differentiable architecture search with early stopping"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "a type of generalization error induced by initialization in deep neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "theory iii dynamics and generalization in deep networks a simple solution"
    - "meta learners learning dynamics are unlike learners"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"

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)