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: "adaptive gradient methods with dynamic bound of learning rate"
  Abstract: Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at this https URL .

Connected through hyperedge HE-1 (induced by source paper "a metric learning reality check"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "unbiased evaluation of deep metric learning algorithms"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "momentum contrast for unsupervised visual representation learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "softtriple loss deep metric learning without triplet sampling"

Connected through hyperedge HE-2 (induced by source paper "laprop a better way to combine momentum with adaptive gradient"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "optimization for deep learning theory and algorithms"
    - "on the variance of the adaptive learning rate and beyond"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "a general system of differential equations to model first order adaptive algorithms"

Connected through hyperedge HE-3 (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"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "analysis of momentum methods"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-4 (induced by source paper "flexible numerical optimization with ensmallen"):
  with members:
    - "on the convergence of adam and beyond"
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "a user friendly hybrid sparse matrix class in c"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "ensmallen a flexible c library for efficient function optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "training gans with optimism"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-5 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"
    - "a survey on soft subspace clustering"

Connected through hyperedge HE-6 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "normalized direction preserving adam"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "an overview of gradient descent optimization algorithms"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-7 (induced by source paper "investigating generalization in neural networks under optimally evolved training perturbations"):
  with members:
    - "built in vulnerabilities to imperceptible adversarial perturbations"
    - "excessive invariance causes adversarial vulnerability"
    - "recurrent world models facilitate policy evolution"
    - "certified defenses for data poisoning attacks"
    - "sharp minima can generalize for deep nets"
    - "improving generalization performance by switching from adam to sgd"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "tadam a robust stochastic gradient optimizer"):
  with members:
    - "on the convergence of adam and beyond"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximal policy optimization algorithms"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "deep residual learning for image recognition"

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)