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 "safe adaptive importance sampling"):
  Abstract: Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed - in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.

Includes members:
  Member "faster coordinate descent via adaptive importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "approximate steepest coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized deep learning with arbitrary communication compression")
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "minding the gaps for block frank wolfe optimization of structured svms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "curvature of feasible sets in offline and online optimization")
  Member "safe screening with variational inequalities and its application to lasso":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")

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)