human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stochastic orthant wise limited memory quasi newton method"):
  Abstract: The $\ell_1$-regularized sparse model has been popular in machine learning society. The orthant-wise quasi-Newton (OWL-QN) method is a representative fast algorithm for training the model. However, the proof of the convergence has been pointed out to be incorrect by multiple sources, and up until now, its convergence has not been proved at all. In this paper, we propose a stochastic OWL-QN method for solving $\ell_1$-regularized problems, both with convex and non-convex loss functions. We address technical difficulties that have existed many years. We propose three alignment steps which are generalized from the the original OWL-QN algorithm, to encourage the parameter update be orthant-wise. We adopt several practical features from recent stochastic variants of L-BFGS and the variance reduction method for subsampled gradients. To the best of our knowledge, this is the first orthant-wise algorithms with comparable theoretical convergence rate with stochastic first order algorithms. We prove a linear convergence rate for our algorithm under strong convexity, and experimentally demonstrate that our algorithm achieves state-of-art performance on $\ell_1$ regularized logistic regression and convolutional neural networks.

Includes members:
  Member "variance reduced second order methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic newton and cubic newton methods with simple local linear quadratic rates")
  Member "res regularized stochastic bfgs algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "optimization with sparsity inducing penalties":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")

Question: Please tell me which class the source paper 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)