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 trust region inexact newton method for large scale machine learning"):
  Abstract: Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this paper, we have proposed a novel stochastic trust region inexact Newton method, called as STRON, to solve large-scale learning problems which uses conjugate gradient to inexactly solve trust region subproblem. The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both, stochastic and full-batch regimes. We have extended STRON using existing variance reduction techniques to deal with the noisy gradients and using preconditioned conjugate gradient as subproblem solver, and empirically proved that they do not work as expected, for the large-scale learning problems. Finally, our empirical results prove efficacy of the proposed method against existing methods with bench marked datasets.

Includes members:
  Member "saags biased stochastic variance reduction methods for large scale learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modified online newton step based on element wise multiplication")
  Member "libs2ml a library for scalable second order machine learning algorithms":
    also connected through other hyperedges:
      (none)
  Member "vr sgd a simple stochastic variance reduction method for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "saags biased stochastic variance reduction methods for large scale learning")
  Member "saags biased stochastic variance reduction methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster algorithms for large scale machine learning using simple sampling techniques")
  Member "faster learning by reduction of data access time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "saags biased stochastic variance reduction methods for large scale learning")
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving sampling accuracy of stochastic gradient mcmc methods via non uniform subsampling of gradients")
  Member "a variance reduced stochastic newton method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast and furious convergence stochastic second order methods under interpolation")
  Member "res regularized stochastic bfgs algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a random feature based newton method for empirical risk minimization in reproducing kernel hilbert space")

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)