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 insufficiency of existing momentum schemes for stochastic optimization"
  Abstract: Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning models, as they often provide significant improvements over stochastic gradient descent (SGD). Rigorously speaking, "fast gradient" methods have provable improvements over gradient descent only for the deterministic case, where the gradients are exact. In the stochastic case, the popular explanations for their wide applicability is that when these fast gradient methods are applied in the stochastic case, they partially mimic their exact gradient counterparts, resulting in some practical gain. This work provides a counterpoint to this belief by proving that there exist simple problem instances where these methods cannot outperform SGD despite the best setting of its parameters. These negative problem instances are, in an informal sense, generic; they do not look like carefully constructed pathological instances. These results suggest (along with empirical evidence) that HB or NAG's practical performance gains are a by-product of mini-batching. #R##N#Furthermore, this work provides a viable (and provable) alternative, which, on the same set of problem instances, significantly improves over HB, NAG, and SGD's performance. This algorithm, referred to as Accelerated Stochastic Gradient Descent (ASGD), is a simple to implement stochastic algorithm, based on a relatively less popular variant of Nesterov's Acceleration. Extensive empirical results in this paper show that ASGD has performance gains over HB, NAG, and SGD.

Connected through hyperedge HE-1 (induced by source paper "the two regimes of deep network training"):
  with members:
    - "an exponential learning rate schedule for deep learning"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "measuring the effects of data parallelism on neural network training"
    - "super convergence very fast training of residual networks using large learning rates"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of nesterov s accelerated gradient method in stochastic settings"):
  with members:
    - "accelerating sgd with momentum for over parameterized learning"
    - "sgd general analysis and improved rates"
    - "understanding the role of momentum in stochastic gradient methods"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "negative momentum for improved game dynamics"
    - "on the curved geometry of accelerated optimization"
    - "linear coupling an ultimate unification of gradient and mirror descent"

Connected through hyperedge HE-3 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"
    - "three mechanisms of weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "momentum based variance reduction in non convex sgd"
    - "on the convergence of adam and beyond"
    - "aggregated momentum stability through passive damping"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"
    - "regularizing deep networks using efficient layerwise adversarial training"

Connected through hyperedge HE-5 (induced by source paper "adaptive weight decay for deep neural networks"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "don t decay the learning rate increase the batch size"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "sharp minima can generalize for deep nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-6 (induced by source paper "an adaptive remote stochastic gradient method for training neural networks"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "on the convergence of adam and beyond"
    - "sadam a variant of adam for strongly convex functions"
    - "scaling sgd batch size to 32k for imagenet training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "training neural networks without gradients a scalable admm approach"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "accelerating sgd with momentum for over parameterized learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "an analysis of deep neural network models for practical applications"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure"):
  with members:
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

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)