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: "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
  Abstract: The choice of batch-size in a stochastic optimization algorithm plays a substantial role for both optimization and generalization. Increasing the batch-size used typically improves optimization but degrades generalization. To address the problem of improving generalization while maintaining optimal convergence in large-batch training, we propose to add covariance noise to the gradients. We demonstrate that the optimization performance of our method is more accurately captured by the structure of the noise covariance matrix rather than by the variance of gradients. Moreover, over the convex-quadratic, we prove in theory that it can be characterized by the Frobenius norm of the noise matrix. Our empirical studies with standard deep learning model-architectures and datasets shows that our method not only improves generalization performance in large-batch training, but furthermore, does so in a way where the optimization performance remains desirable and the training duration is not elongated.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards explaining the regularization effect of initial large learning rate in training neural networks"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what can resnet learn efficiently going beyond kernels"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "the multiplicative noise in stochastic gradient descent data dependent regularization continuous and discrete approximation"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform in time weak error analysis for stochastic gradient descent algorithms via diffusion approximation"
    - "an empirical study on regularization of deep neural networks by local rademacher complexity"
    - "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
    - "three factors influencing minima in sgd"
    - "an alternative view when does sgd escape local minima"
    - "escaping saddles with stochastic gradients"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-4 (induced by source paper "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "information matrices and generalization"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "signsgd compressed optimisation for non convex problems"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "gradient noise convolution gnc smoothing loss function for distributed large batch sgd"):
  with members:
    - "an alternative view when does sgd escape local minima"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "imagenet training in minutes"
    - "random erasing data augmentation"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-6 (induced by source paper "on the noisy gradient descent that generalizes as sgd"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "three factors influencing minima in sgd"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "limitations of the empirical fisher approximation"):
  with members:
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "large scale distributed second order optimization using kronecker factored approximate curvature for deep convolutional neural networks"
    - "noisy natural gradient as variational inference"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward networks"

Connected through hyperedge HE-8 (induced by source paper "don t use large mini batches use local sgd"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "federated learning strategies for improving communication efficiency"
    - "an empirical model of large batch training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"

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)