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: "a unified analysis of stochastic momentum methods for deep learning"
  Abstract: Stochastic momentum methods have been widely adopted in training deep neural networks. However, their theoretical analysis of convergence of the training objective and the generalization error for prediction is still under-explored. This paper aims to bridge the gap between practice and theory by analyzing the stochastic gradient (SG) method, and the stochastic momentum methods including two famous variants, i.e., the stochastic heavy-ball (SHB) method and the stochastic variant of Nesterov's accelerated gradient (SNAG) method. We propose a framework that unifies the three variants. We then derive the convergence rates of the norm of gradient for the non-convex optimization problem, and analyze the generalization performance through the uniform stability approach. Particularly, the convergence analysis of the training objective exhibits that SHB and SNAG have no advantage over SG. However, the stability analysis shows that the momentum term can improve the stability of the learned model and hence improve the generalization performance. These theoretical insights verify the common wisdom and are also corroborated by our empirical analysis on deep learning.

Connected through hyperedge HE-1 (induced by source paper "revisiting sgd with increasingly weighted averaging optimization and generalization perspectives"):
  with members:
    - "deep residual learning for image recognition"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "deep learning with elastic averaging sgd"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "time delay momentum a regularization perspective on the convergence and generalization of stochastic momentum for deep learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "decoupled weight decay regularization"
    - "data dependent stability of stochastic gradient descent"
    - "a pac bayesian analysis of randomized learning with application to stochastic gradient descent"
    - "eve a gradient based optimization method with locally and globally adaptive learning rates"

Connected through hyperedge HE-3 (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"
    - "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"
    - "stochastic gradient push for distributed deep learning"
    - "step size matters in 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"

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)