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: "on the convergence of weighted adagrad with momentum for training deep neural networks"
  Abstract: Adaptive stochastic gradient descent methods, such as AdaGrad, RMSProp, Adam, AMSGrad, etc., have been demonstrated efficacious in solving non-convex stochastic optimization, such as training deep neural networks. However, their convergence rates have not been touched under the non-convex stochastic circumstance except recent breakthrough results on AdaGrad, perturbed AdaGrad and AMSGrad. In this paper, we propose two new adaptive stochastic gradient methods called AdaHB and AdaNAG which integrate a novel weighted coordinate-wise AdaGrad with heavy ball momentum and Nesterov accelerated gradient momentum, respectively. The $\mathcal{O}(\frac{\log{T}}{\sqrt{T}})$ non-asymptotic convergence rates of AdaHB and AdaNAG in non-convex stochastic setting are also jointly established by leveraging a newly developed unified formulation of these two momentum mechanisms. Moreover, comparisons have been made between AdaHB, AdaNAG, Adam and RMSProp, which, to a certain extent, explains the reasons why Adam and RMSProp are divergent. In particular, when momentum term vanishes we obtain convergence rate of coordinate-wise AdaGrad in non-convex stochastic setting as a byproduct.

Connected through hyperedge HE-1 (induced by source paper "on empirical comparisons of optimizers for deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "on the variance of the adaptive learning rate and beyond"
    - "namsg an efficient method for training neural networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "blockwise adaptivity faster training and better generalization in deep learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

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)