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: "stochastic modified equations and dynamics of stochastic gradient algorithms i mathematical foundations"
  Abstract: We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is approximated by a class of stochastic differential equations with small noise parameters. We prove that this approximation can be understood mathematically as an weak approximation, which leads to a number of precise and useful results on the approximations of stochastic gradient descent (SGD), momentum SGD and stochastic Nesterov's accelerated gradient method in the general setting of stochastic objectives. We also demonstrate through explicit calculations that this continuous-time approach can uncover important analytical insights into the stochastic gradient algorithms under consideration that may not be easy to obtain in a purely discrete-time setting.

Connected through hyperedge HE-1 (induced by source paper "stochasticity of deterministic gradient descent large learning rate for multiscale objective function"):
  with members:
    - "variational optimization on lie groups with examples of leading generalized eigenvalue problems"
    - "analysis of momentum methods"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "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-2 (induced by source paper "stochastic runge kutta methods and adaptive sgd g2 stochastic gradient descent"):
  with members:
    - "assessing four neural networks on handwritten digit recognition dataset mnist"
    - "deep neural networks motivated by partial differential equations"
    - "convolutional neural networks combined with runge kutta methods"
    - "super convergence very fast training of residual networks using large learning rates"
    - "cyclical learning rates for training neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "stochastic approximation for canonical correlation analysis"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "an overview of gradient descent optimization algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"):
  with members:
    - "on the local minima of the empirical risk"
    - "an alternative view when does sgd escape local minima"
    - "escaping saddles with stochastic gradients"
    - "geometry of optimization and implicit regularization in deep learning"
    - "opening the black box of deep neural networks via information"
    - "how to escape saddle points efficiently"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-5 (induced by source paper "uniform in time weak error analysis for stochastic gradient descent algorithms via diffusion approximation"):
  with members:
    - "improving generalization performance by switching from adam to sgd"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

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)