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: "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
  Abstract: Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is added to an unbiased estimate of the gradient at each iteration. This modest change allows SGLD to escape local minima and suffices to guarantee asymptotic convergence to global minimizers for sufficiently regular non-convex objectives (Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis in the context of non-convex learning problems, giving finite-time guarantees for SGLD to find approximate minimizers of both empirical and population risks. As in the asymptotic setting, our analysis relates the discrete-time SGLD Markov chain to a continuous-time diffusion process. A new tool that drives the results is the use of weighted transportation cost inequalities to quantify the rate of convergence of SGLD to a stationary distribution in the Euclidean $2$-Wasserstein distance.

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"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-2 (induced by source paper "amagold amortized metropolis adjustment for efficient stochastic gradient mcmc"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "minibatch gibbs sampling on large graphical models"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "scalable bayesian learning of recurrent neural networks for language modeling"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-3 (induced by source paper "stochasticrank global optimization of scale free discrete functions"):
  with members:
    - "sglb stochastic gradient langevin boosting"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "sglb stochastic gradient langevin boosting"):
  with members:
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (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-6 (induced by source paper "theory iii dynamics and generalization in deep networks a simple solution"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "loss landscapes of regularized linear autoencoders"
    - "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"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-7 (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-8 (induced by source paper "chaining meets chain rule multilevel entropic regularization and training of neural nets"):
  with members:
    - "distribution dependent analysis of gibbs erm principle"
    - "tightening mutual information based bounds on generalization error"
    - "data dependent pac bayes priors via differential privacy"
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

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)