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: "a tail index analysis of stochastic gradient noise in deep neural networks"
  Abstract: The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the classical central limit theorem (CLT) kicks in. This assumption is often made for mathematical convenience, since it enables SGD to be analyzed as a stochastic differential equation (SDE) driven by a Brownian motion. We argue that the Gaussianity assumption might fail to hold in deep learning settings and hence render the Brownian motion-based analyses inappropriate. Inspired by non-Gaussian natural phenomena, we consider the GN in a more general context and invoke the generalized CLT (GCLT), which suggests that the GN converges to a heavy-tailed $\alpha$-stable random variable. Accordingly, we propose to analyze SGD as an SDE driven by a L\'{e}vy motion. Such SDEs can incur `jumps', which force the SDE transition from narrow minima to wider minima, as proven by existing metastability theory. To validate the $\alpha$-stable assumption, we conduct extensive experiments on common deep learning architectures and show that in all settings, the GN is highly non-Gaussian and admits heavy-tails. We further investigate the tail behavior in varying network architectures and sizes, loss functions, and datasets. Our results open up a different perspective and shed more light on the belief that SGD prefers wide minima.

Connected through hyperedge HE-1 (induced by source paper "unique properties of wide minima in deep networks"):
  with members:
    - "implicit regularization of normalization methods"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "step size matters in deep learning"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-2 (induced by source paper "a diffusion theory for deep learning dynamics stochastic gradient descent escapes from sharp minima exponentially fast"):
  with members:
    - "where is the information in a deep neural network"
    - "asymmetric valleys beyond sharp and flat local minima"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "don t decay the learning rate increase the batch size"
    - "sharp minima can generalize for deep nets"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-3 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "stiffness a new perspective on generalization in neural networks"
    - "circuit based intrinsic methods to detect overfitting"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "deep learning is robust to massive label noise"
    - "the global optimization geometry of shallow linear neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-4 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the intrinsic privacy of stochastic gradient descent"):
  with members:
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "evaluating differentially private machine learning in practice"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep reinforcement learning that matters"
    - "differentially private federated learning a client level perspective"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"

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:
    - "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"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-7 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "asymptotics of wide networks from feynman diagrams"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"

Connected through hyperedge HE-8 (induced by source paper "on the noisy gradient descent that generalizes as sgd"):
  with members:
    - "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"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "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"

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)