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 "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  Abstract: In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD with a new method called two-step preconditioning to achieve an approximate solution with a time complexity lower than that of the state-of-the-art techniques for the low precision case. Our idea can also be extended to the high precision case, which gives an alternative implementation to the Iterative Hessian Sketch (IHS) method with significantly improved time complexity. Experiments on benchmark and synthetic datasets suggest that our methods indeed outperform existing ones considerably in both the low and high precision cases.

Includes members:
  Member "solving ridge regression using sketched preconditioned svrg":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization")
  Member "randomized block krylov methods for stronger and faster approximate singular value decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "a practical guide to randomized matrix computations with matlab implementations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weighted gradient coding with leverage score sampling")
  Member "faster sgd using sketched conditioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "second order optimization made practical")
  Member "implementing randomized matrix algorithms in parallel and distributed environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gpu accelerated sub sampled newton s method")
  Member "randomized sketches of convex programs with sharp guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weighted gradient coding with leverage score sampling")
  Member "mini batch primal and dual methods for svms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "recovering the optimal solution by dual random projection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")

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)