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 "dynanewton accelerating newton s method for machine learning"):
  Abstract: Newton's method is a fundamental technique in optimization with quadratic convergence within a neighborhood around the optimum. However reaching this neighborhood is often slow and dominates the computational costs. We exploit two properties specific to empirical risk minimization problems to accelerate Newton's method, namely, subsampling training data and increasing strong convexity through regularization. We propose a novel continuation method, where we define a family of objectives over increasing sample sizes and with decreasing regularization strength. Solutions on this path are tracked such that the minimizer of the previous objective is guaranteed to be within the quadratic convergence region of the next objective to be optimized. Thereby every Newton iteration is guaranteed to achieve super-linear contractions with regard to the chosen objective, which becomes a moving target. We provide a theoretical analysis that motivates our algorithm, called DynaNewton, and characterizes its speed of convergence. Experiments on a wide range of data sets and problems consistently confirm the predicted computational savings.

Includes members:
  Member "starting small learning with adaptive sample sizes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "x traincaps accelerated training of capsule nets through lightweight software optimizations")
  Member "reducing runtime by recycling samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "starting small learning with adaptive sample sizes")
  Member "on graduated optimization for stochastic non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "variance reduced stochastic gradient descent with neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "federated learning of a mixture of global and local models")
  Member "res regularized stochastic bfgs algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "self concordant analysis for logistic regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)