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: "global convergence of adaptive gradient methods for an over parameterized neural network"
  Abstract: Adaptive gradient methods like AdaGrad are widely used in optimizing neural networks. Yet, existing convergence guarantees for adaptive gradient methods require either convexity or smoothness, and, in the smooth setting, only guarantee convergence to a stationary point. We propose an adaptive gradient method and show that for two-layer over-parameterized neural networks -- if the width is sufficiently large (polynomially) -- then the proposed method converges \emph{to the global minimum} in polynomial time, and convergence is robust, \emph{ without the need to fine-tune hyper-parameters such as the step-size schedule and with the level of over-parametrization independent of the training error}. Our analysis indicates in particular that over-parametrization is crucial for the harnessing the full potential of adaptive gradient methods in the setting of neural networks.

Connected through hyperedge HE-1 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "contrastive multiview coding"
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "momentum contrast for unsupervised visual representation learning"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive federated optimization"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "federated learning challenges methods and future directions"
    - "on the convergence of adam and beyond"
    - "the non iid data quagmire of decentralized machine learning"
    - "measuring the effects of non identical data distribution for federated visual classification"

Connected through hyperedge HE-3 (induced by source paper "global convergence of deep networks with one wide layer followed by pyramidal topology"):
  with members:
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on connected sublevel sets in deep learning"
    - "neural networks learning and memorization with almost no over parameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"

Connected through hyperedge HE-4 (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"
    - "analysis of momentum methods"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-5 (induced by source paper "balancedness and alignment are unlikely in linear neural networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "memorization in overparameterized autoencoders"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-6 (induced by source paper "towards understanding the spectral bias of deep learning"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on learning over parameterized neural networks a functional approximation prospective"

Connected through hyperedge HE-7 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-8 (induced by source paper "an improved analysis of training over parameterized deep neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural 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)