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: "res regularized stochastic bfgs algorithm"
  Abstract: RES, a regularized stochastic version of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method, is proposed to solve strongly convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second-order methods, on the other hand, is impracticable because the computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost, RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian eigenvalues of the sample functions are sufficient to guarantee almost sure convergence of a subsequence generated by RES and convergence of the sequence in expectation to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.

Connected through hyperedge HE-1 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning"):
  with members:
    - "saags biased stochastic variance reduction methods for large scale learning"
    - "libs2ml a library for scalable second order machine learning algorithms"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "saags biased stochastic variance reduction methods"
    - "faster learning by reduction of data access time"
    - "importance sampling for minibatches"
    - "a variance reduced stochastic newton method"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-2 (induced by source paper "a random feature based newton method for empirical risk minimization in reproducing kernel hilbert space"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-3 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "a stochastic quasi newton method with nesterov s accelerated gradient"):
  with members:
    - "accelerating minibatch stochastic gradient descent using typicality sampling"
    - "implementation of stochastic quasi newton s method in pytorch"
    - "implementation of a modified nesterov s accelerated quasi newton method on tensorflow"
    - "a variance reduced stochastic newton method"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "stochastic conjugate gradient algorithm with variance reduction"):
  with members:
    - "on the convergence of adam and beyond"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "neumann optimizer a practical optimization algorithm for deep neural networks"):
  with members:
    - "imagenet training in minutes"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "scaling sgd batch size to 32k for imagenet training"
    - "tacotron towards end to end speech synthesis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "hemingway modeling distributed optimization algorithms"):
  with members:
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "learning to compose neural networks for question answering"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "designing neural network architectures using reinforcement learning"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-8 (induced by source paper "fast online low rank tensor subspace tracking by cp decomposition using recursive least squares from incomplete observations"):
  with members:
    - "inductive framework for multi aspect streaming tensor completion with side information"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"

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)