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: "practical inexact proximal quasi newton method with global complexity analysis"
  Abstract: Recently several methods were proposed for sparse optimization which make careful use of second-order information [10, 28, 16, 3] to improve local convergence rates. These methods construct a composite quadratic approximation using Hessian information, optimize this approximation using a first-order method, such as coordinate descent and employ a line search to ensure sufficient descent. Here we propose a general framework, which includes slightly modified versions of existing algorithms and also a new algorithm, which uses limited memory BFGS Hessian approximations, and provide a novel global convergence rate analysis, which covers methods that solve subproblems via coordinate descent.

Connected through hyperedge HE-1 (induced by source paper "a distributed quasi newton algorithm for primal and dual regularized empirical risk minimization"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "using neural networks to detect line outages from pmu data"
    - "proximal quasi newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "mllib machine learning in apache spark"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "duality between subgradient and conditional gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-2 (induced by source paper "proximal quasi newton methods for regularized convex optimization with linear and accelerated sublinear convergence rates"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "proximal quasi newton methods for convex optimization"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"

Connected through hyperedge HE-4 (induced by source paper "fused multiple graphical lasso"):
  with members:
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "sparse inverse covariance selection via alternating linearization methods"

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)