human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
  Abstract: In this paper we generalize the framework of the feasible descent method (FDM) to a randomized (R-FDM) and a coordinate-wise random feasible descent method (RC-FDM) framework. We show that the famous SDCA algorithm for optimizing the SVM dual problem, or the stochastic coordinate descent method for the LASSO problem, fits into the framework of RC-FDM. We prove linear convergence for both R-FDM and RC-FDM under the weak strong convexity assumption. Moreover, we show that the duality gap converges linearly for RC-FDM, which implies that the duality gap also converges linearly for SDCA applied to the SVM dual problem.

Connected through hyperedge HE-1 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-2 (induced by source paper "primal dual rates and certificates"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed optimization with arbitrary local solvers"
    - "sdca without duality"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "mini batch primal and dual methods for svms"
    - "an equivalence between the lasso and support vector machines"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-3 (induced by source paper "linear convergence of gradient and proximal gradient methods under the polyak l ojasiewicz condition"):
  with members:
    - "linear convergence of variance reduced stochastic gradient without strong convexity"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-4 (induced by source paper "distributed optimization with arbitrary local solvers"):
  with members:
    - "importance sampling for minibatches"
    - "primal dual rates and certificates"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "distributed inexact damped newton method data partitioning and load balancing"
    - "adding vs averaging in distributed primal dual optimization"
    - "federated optimization distributed optimization beyond the datacenter"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "sdca without duality"

Question: Please tell me which class the center node 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)