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 "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"):
  Abstract: We proposed a doubly stochastic primal-dual coordinate (DSPDC) optimization algorithm for empirical risk minimization, which can be formulated as a bilinear saddle-point problem. In each iteration, our method randomly samples a block of coordinates of the primal and dual solutions to update. The convergence of our method is established in both the distance from the current iterate to the optimal solution and the primal-dual objective gap. We show that the proposed method has a lower overall complexity than existing coordinate methods when either the data matrix has a factorized structure or the proximal mapping on each block is computationally expensive, e.g., involves an eigenvalue decomposition. Furthermore, we give a theoretical lower bound on the iteration complexity of a general family of primal-dual (block) coordinate methods for bilinear saddle-point problems, which also includes DSPDC.

Includes members:
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "mini batch semi stochastic gradient descent in the proximal setting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources")
  Member "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust coreset construction for distributed machine learning")
  Member "communication complexity of distributed convex learning and optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "finito a faster permutable incremental gradient method for big data problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "scalable kernel methods via doubly stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural kernels without tangents")
  Member "semi stochastic coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning")
  Member "saga a fast incremental gradient method with support for non strongly convex composite objectives":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

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)