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: "generalization bounds of sgld for non convex learning two theoretical viewpoints"
  Abstract: Algorithm-dependent generalization error bounds are central to statistical learning theory. A learning algorithm may use a large hypothesis space, but the limited number of iterations controls its model capacity and generalization error. The impacts of stochastic gradient methods on generalization error for non-convex learning problems not only have important theoretical consequences, but are also critical to generalization errors of deep learning. #R##N#In this paper, we study the generalization errors of Stochastic Gradient Langevin Dynamics (SGLD) with non-convex objectives. Two theories are proposed with non-asymptotic discrete-time analysis, using Stability and PAC-Bayesian results respectively. The stability-based theory obtains a bound of $O\left(\frac{1}{n}L\sqrt{\beta T_k}\right)$, where $L$ is uniform Lipschitz parameter, $\beta$ is inverse temperature, and $T_k$ is aggregated step sizes. For PAC-Bayesian theory, though the bound has a slower $O(1/\sqrt{n})$ rate, the contribution of each step is shown with an exponentially decaying factor by imposing $\ell^2$ regularization, and the uniform Lipschitz constant is also replaced by actual norms of gradients along trajectory. Our bounds have no implicit dependence on dimensions, norms or other capacity measures of parameter, which elegantly characterizes the phenomenon of "Fast Training Guarantees Generalization" in non-convex settings. This is the first algorithm-dependent result with reasonable dependence on aggregated step sizes for non-convex learning, and has important implications to statistical learning aspects of stochastic gradient methods in complicated models such as deep learning.

Connected through hyperedge HE-1 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-2 (induced by source paper "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"):
  with members:
    - "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"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "interplay between optimization and generalization of stochastic gradient descent with covariance noise"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "a coordinate free construction of scalable natural gradient"
    - "three mechanisms of weight decay regularization"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "on generalization error bounds of noisy gradient methods for non convex learning"):
  with members:
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "on exact computation with an infinitely wide neural net"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-5 (induced by source paper "generalization in generative adversarial networks a novel perspective from privacy protection"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "srpgan perceptual generative adversarial network for single image super resolution"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-6 (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"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "three factors influencing minima in sgd"
    - "an alternative view when does sgd escape local minima"
    - "escaping saddles with stochastic gradients"

Connected through hyperedge HE-7 (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"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "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-8 (induced by source paper "characterizing membership privacy in stochastic gradient langevin dynamics"):
  with members:
    - "generalization in generative adversarial networks a novel perspective from privacy protection"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"
    - "dropblock a regularization method for convolutional networks"
    - "generalization error bounds for noisy iterative algorithms"
    - "understanding membership inferences on well generalized learning models"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "wide residual 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)