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 "fast and strong convergence of online learning algorithms"):
  Abstract: In this paper, we study the online learning algorithm without explicit regularization terms. This algorithm is essentially a stochastic gradient descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially decaying step size in each iteration can play a role of regularization to ensure the generalization ability of online learning algorithm. We develop a novel capacity dependent analysis on the performance of the last iterate of online learning algorithm. The contribution of this paper is two-fold. First, our nice analysis can lead to the convergence rate in the standard mean square distance which is the best so far. Second, we establish, for the first time, the strong convergence of the last iterate with polynomially decaying step sizes in the RKHS norm. We demonstrate that the theoretical analysis established in this paper fully exploits the fine structure of the underlying RKHS, and thus can lead to sharp error estimates of online learning algorithm.

Includes members:
  Member "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate")
  Member "convergence of unregularized online learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic gradient descent for nonconvex learning without bounded gradient assumptions")
  Member "optimal learning for multi pass stochastic gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate")
  Member "distributed learning with regularized least squares":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalised lipschitz regularisation equals distributional robustness")
  Member "unregularized online learning algorithms with general loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic iterative hard thresholding for graph structured sparsity optimization")
  Member "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "making gradient descent optimal for strongly convex stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wireless federated learning with local differential privacy")

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)