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: "analysis of distributed stochastic dual coordinate ascent"
  Abstract: In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates. However, no serious analysis has been provided to understand the updates and therefore the convergence rates. In the paper, we bridge the gap by providing a theoretical analysis of the convergence rates of the practical DisDCA algorithm. Our analysis helped by empirical studies has shown that it could yield an exponential speed-up in the convergence by increasing the number of dual updates at each iteration. This result justifies the superior performances of the practical DisDCA as compared to the naive variant. As a byproduct, our analysis also reveals the convergence behavior of the one-communication DisDCA.

Connected through hyperedge HE-1 (induced by source paper "central server free federated learning over single sided trust social networks"):
  with members:
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "introduction to online convex optimization"
    - "federated machine learning concept and applications"
    - "asynchronous subgradient push"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "stochastic gradient push for distributed deep learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a general distributed dual coordinate optimization framework for regularized loss minimization"):
  with members:
    - "cocoa a general framework for communication efficient distributed optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "an efficient distributed learning algorithm based on effective local functional approximations"

Connected through hyperedge HE-4 (induced by source paper "distributed inexact damped newton method data partitioning and load balancing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "parallel coordinate descent for l1 regularized loss minimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-5 (induced by source paper "cocoa a general framework for communication efficient distributed optimization"):
  with members:
    - "distributed optimization with arbitrary local solvers"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"

Connected through hyperedge HE-6 (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"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "sdna stochastic dual newton ascent for empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "adding vs averaging in distributed primal dual optimization"):
  with members:
    - "distributed optimization with arbitrary local solvers"
    - "communication efficient distributed dual coordinate ascent"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-8 (induced by source paper "distributed mini batch sdca"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adding vs averaging in distributed primal dual optimization"
    - "parallel coordinate descent for l1 regularized loss minimization"
    - "optimal distributed online prediction using mini batches"

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.DC(Distributed, Parallel, and Cluster Computing)