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: "dynamics of stochastic gradient algorithms"
  Abstract: Stochastic gradient algorithms (SGA) are increasingly popular in machine learning applications and have become "the algorithm" for extremely large scale problems. Although there are some convergence results, little is known about their dynamics. In this paper, We propose the method of stochastic modified equations (SME) to analyze the dynamics of the SGA. Using this technique, we can give precise characterizations for both the initial convergence speed and the eventual oscillations, at least in some special cases. Furthermore, the SME formalism allows us to characterize various speed-up techniques, such as introducing momentum, adjusting the learning rate and the mini-batch sizes. Previously, these techniques relied mostly on heuristics. Besides introducing simple examples to illustrate the SME formalism, we also apply the framework to improve the relaxed randomized Kaczmarz method for solving linear equations. The SME framework is a precise and unifying approach to understanding and improving the SGA, and has the potential to be applied to many more stochastic algorithms.

Connected through hyperedge HE-1 (induced by source paper "stochastic gradient descent with random learning rate"):
  with members:
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "cyclical learning rates for training neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "interplay between optimization and generalization of stochastic gradient descent with covariance noise"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "a coordinate free construction of scalable natural gradient"
    - "three mechanisms of weight decay regularization"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-3 (induced by source paper "layer dynamics of linearised neural nets"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "theory iiib generalization in deep networks"
    - "lstm a search space odyssey"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "theory ii landscape of the empirical risk in deep learning"
    - "identity matters in deep learning"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-4 (induced by source paper "deep relaxation partial differential equations for optimizing deep neural networks"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"
    - "noisy activation functions"
    - "training recurrent neural networks by diffusion"
    - "trivializing the energy landscape of deep networks"

Connected through hyperedge HE-5 (induced by source paper "don t decay the learning rate increase the batch size"):
  with members:
    - "imagenet training in minutes"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "generalization bounds of sgld for non convex learning two theoretical viewpoints"):
  with members:
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "how to escape saddle points efficiently"
    - "optimal learning for multi pass stochastic gradient methods"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "understanding deep learning requires rethinking generalization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-7 (induced by source paper "distributed stochastic optimization for deep learning thesis"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"
    - "distributed deep learning for answer selection"
    - "convolutional neural networks with low rank regularization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "staleness aware async sgd for distributed deep learning"
    - "the loss surfaces of multilayer networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)