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: "step size matters in deep learning"
  Abstract: Training a neural network with the gradient descent algorithm gives rise to a discrete-time nonlinear dynamical system. Consequently, behaviors that are typically observed in these systems emerge during training, such as convergence to an orbit but not to a fixed point or dependence of convergence on the initialization. Step size of the algorithm plays a critical role in these behaviors: it determines the subset of the local optima that the algorithm can converge to, and it specifies the magnitude of the oscillations if the algorithm converges to an orbit. To elucidate the effects of the step size on training of neural networks, we study the gradient descent algorithm as a discrete-time dynamical system, and by analyzing the Lyapunov stability of different solutions, we show the relationship between the step size of the algorithm and the solutions that can be obtained with this algorithm. The results provide an explanation for several phenomena observed in practice, including the deterioration in the training error with increased depth, the hardness of estimating linear mappings with large singular values, and the distinct performance of deep residual networks.

Connected through hyperedge HE-1 (induced by source paper "unique properties of wide minima in deep networks"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "implicit regularization of normalization methods"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-2 (induced by source paper "at stability s edge how to adjust hyperparameters to preserve minima selection in asynchronous training of neural networks"):
  with members:
    - "taming momentum in a distributed asynchronous environment"
    - "a unified analysis of stochastic momentum methods for deep learning"
    - "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"
    - "stochastic gradient push for distributed deep learning"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "wide residual networks"

Connected through hyperedge HE-3 (induced by source paper "implicit regularization of discrete gradient dynamics in linear neural networks"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "a mathematical theory of semantic development in deep neural networks"
    - "on the learning dynamics of deep neural networks"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"
    - "understanding deep learning requires rethinking generalization"
    - "path sgd path normalized optimization in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "persistency of excitation for robustness of neural networks"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "risk and parameter convergence of logistic regression"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "understanding deep learning requires rethinking generalization"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "augment your batch better training with larger batches"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "image classification at supercomputer scale"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "revisiting small batch training for deep neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"

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)