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: "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
  Abstract: RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical convergence properties have remained unclear. Further, recent work has seemed to suggest that these algorithms have worse generalization properties when compared to carefully tuned stochastic gradient descent or its momentum variants. In this work, we make progress towards a deeper understanding of ADAM and RMSProp in two ways. First, we provide proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives, and we give bounds on the running time. #R##N#Next we design experiments to empirically study the convergence and generalization properties of RMSProp and ADAM against Nesterov's Accelerated Gradient method on a variety of common autoencoder setups and on VGG-9 with CIFAR-10. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter $\beta_1$. We show that at very high values of the momentum parameter ($\beta_1 = 0.99$) ADAM outperforms a carefully tuned NAG on most of our experiments, in terms of getting lower training and test losses. On the other hand, NAG can sometimes do better when ADAM's $\beta_1$ is set to the most commonly used value: $\beta_1 = 0.9$, indicating the importance of tuning the hyperparameters of ADAM to get better generalization performance. #R##N#We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms, and it also produces iterates which exhibit an increasing trend for the minimum eigenvalue of the Hessian of the loss function at the iterates.

Connected through hyperedge HE-1 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate"):
  with members:
    - "calibrating the adaptive learning rate to improve convergence of adam"
    - "on empirical comparisons of optimizers for deep learning"
    - "decaying momentum helps neural network training"
    - "a survey of optimization methods from a machine learning perspective"
    - "understanding the role of momentum in stochastic gradient methods"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "analysis of momentum methods"

Connected through hyperedge HE-2 (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-3 (induced by source paper "calibrating the adaptive learning rate to improve convergence of adam"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "an alternative view when does sgd escape local minima"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (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"
    - "a unified analysis of stochastic momentum methods for deep learning"
    - "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-5 (induced by source paper "sadam a variant of adam for strongly convex functions"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "gadam genetic evolutionary adam for deep neural network optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "stronger baselines for trustable results in neural machine translation"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-6 (induced by source paper "rapidly adapting moment estimation"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a genetic algorithm based kernel size selection approach for a multi column convolutional neural network"):
  with members:
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
    - "neural architecture search with reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "an enhanced harmony search method for bangla handwritten character recognition using region sampling"
    - "roulette wheel selection via stochastic acceptance"

Connected through hyperedge HE-8 (induced by source paper "a sufficient condition for convergences of adam and rmsprop"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "nostalgic adam weighing more of the past gradients when designing the adaptive learning rate"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"

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)