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: "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.

Connected through hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient federated learning over wireless communication networks"
    - "supervised learning under distributed features"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "revisiting distributed synchronous sgd"
    - "adding vs averaging in distributed primal dual optimization"
    - "fast distributed asynchronous sgd with variance reduction"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-2 (induced by source paper "communication efficient distributed dual coordinate ascent for machine learning in general tree networks"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-3 (induced by source paper "asynchronous stochastic block coordinate descent with variance reduction"):
  with members:
    - "asynchronous stochastic proximal optimization algorithms with variance reduction"
    - "sdca without duality regularization and individual convexity"
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "revisiting asynchronous linear solvers provable convergence rate through randomization"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-4 (induced by source paper "asynchronous stochastic gradient descent with variance reduction for non convex optimization"):
  with members:
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "asynchronous stochastic block coordinate descent with variance reduction"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "a distributed asynchronous and incremental algorithm for nonconvex optimization an admm based approach"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)