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: "laplacian smoothing gradient descent"
  Abstract: We propose a class of very simple modifications of gradient descent and stochastic gradient descent. We show that when applied to a large variety of machine learning problems, ranging from logistic regression to deep neural nets, the proposed surrogates can dramatically reduce the variance, allow to take a larger step size, and improve the generalization accuracy. The methods only involve multiplying the usual (stochastic) gradient by the inverse of a positive definitive matrix (which can be computed efficiently by FFT) with a low condition number coming from a one-dimensional discrete Laplacian or its high order generalizations. It also preserves the mean and increases the smallest component and decreases the largest component. The theory of Hamilton-Jacobi partial differential equations demonstrates that the implicit version of the new algorithm is almost the same as doing gradient descent on a new function which (i) has the same global minima as the original function and (ii) is ``more convex". Moreover, we show that optimization algorithms with these surrogates converge uniformly in the discrete Sobolev $H_\sigma^p$ sense and reduce the optimality gap for convex optimization problems. The code is available at: \url{this https URL}

Connected through hyperedge HE-1 (induced by source paper "scheduled restart momentum for accelerated stochastic gradient descent"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "on the convergence of adam and beyond"
    - "understanding generalization through visualizations"
    - "pytorch an imperative style high performance deep learning library"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "graph interpolating activation improves both natural and robust accuracies in data efficient deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "mathematical analysis of adversarial attacks"
    - "deep neural nets with interpolating function as output activation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"):
  with members:
    - "efficient privacy preserving nonconvex optimization"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "differentially private federated learning a client level perspective"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "local differential privacy for physical sensor data and sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "a deterministic approach to avoid saddle points"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "the power of normalization faster evasion of saddle points"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-5 (induced by source paper "a study on graph structured recurrent neural networks and sparsification with application to epidemic forecasting"):
  with members:
    - "graph based deep modeling and real time forecasting of sparse spatio temporal data"
    - "deep learning for real time crime forecasting and its ternarization"
    - "structural rnn deep learning on spatio temporal graphs"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "laplacian smoothing stochastic gradient markov chain monte carlo"):
  with members:
    - "a deterministic approach to avoid saddle points"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-7 (induced by source paper "mathematical analysis of adversarial attacks"):
  with members:
    - "deep learning with data dependent implicit activation function"
    - "deep neural nets with interpolating function as output activation"
    - "adversarial defense via data dependent activation function and total variation minimization"
    - "black box adversarial attacks with limited queries and information"
    - "sok security and privacy in machine learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "stochastic training of residual networks a differential equation viewpoint"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "learning across scales a multiscale method for convolution neural networks"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "deep residual learning and pdes on manifold"
    - "don t decay the learning rate increase the batch size"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "deep networks with stochastic depth"

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)