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 "distributed asynchronous dual free stochastic dual coordinate ascent"):
  Abstract: With the boom of data, it is challenging to optimize a large-scale machine learning problem efficiently. Both of the convergence rate and scalability are nontrivial for an optimization method. Stochastic gradient descent (SGD) is widely applied in large-scale optimization, and it sacrifices its convergence rate for a lower computation complexity per iteration. Recently, variance reduction technique is proposed to accelerate the convergence of stochastic method, for example stochastic variance reduced gradient (SVRG), stochastic duall coordinate ascent (SDCA) or dual free stochastic dual coordinate ascent (dfSDCA). However, serial algorithm is not applicable when the data can not be stored in a single computer. In this paper, we propose Distributed Asynchronous Dual Free Coordinate Ascent method (dis-dfSDCA), and prove that it has linear convergence rate when the problem is convex and smooth. The stale gradient update is common in asynchronous method, and the effect of staleness on the performance of our method is also analyzed in the paper. We conduct experiments on large-scale data from LIBSVM on Amazon Web Services and experimental results demonstrate our analysis.

Includes members:
  Member "sdca without duality regularization and individual convexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of minimizing convex finite sums without using the indices of the individual functions")
  Member "asynchronous stochastic gradient descent with variance reduction for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive weight decay for deep neural networks")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "on variance reduction in stochastic gradient descent and its asynchronous variants":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "asynchronous distributed semi stochastic gradient optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed momentum for byzantine resilient learning")
  Member "sdca without duality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport")
  Member "distributed mini batch sdca":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic distributed and federated optimization for machine learning")
  Member "improved svrg for non strongly convex or sum of non convex objectives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variance reduction with sparse gradients")

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)