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: "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.

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)