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 "linear coupling an ultimate unification of gradient and mirror descent"):
  Abstract: First-order methods play a central role in large-scale convex optimization. Even though many variations exist, each suited to a particular problem form, almost all such methods fundamentally rely on two types of algorithmic steps and two corresponding types of analysis: gradient-descent steps, which yield primal progress, and mirror-descent steps, which yield dual progress. In this paper, we observe that the performances of these two types of step are complementary, so that faster algorithms can be designed by linearly coupling the two steps. #R##N#In particular, we obtain a simple accelerated gradient method for the class of smooth convex optimization problems. The first such method was proposed by Nesterov back to 1983, but to the best of our knowledge, the proof of the fast convergence of accelerated gradient methods has not found a clear interpretation and is still regarded by many as crucially relying on "algebraic tricks". We apply our novel insights to construct a new accelerated gradient method as a natural linear coupling of gradient descent and mirror descent and to write its proof of convergence as a simple combination of the convergence analyses of the two underlying descent steps. #R##N#We believe that the complementary view and the linear coupling technique in this paper will prove very useful in the design of first-order methods as it allows us to design fast algorithms in a conceptually easier way. For instance, our technique greatly facilitates the recent breakthroughs in solving packing and covering linear programs [AO14, AO15].

Includes members:
  Member "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")
  Member "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "navigating central path with electrical flows from flows to matchings and back":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "nearly maximum flows in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "a unified view of regularized dual averaging and mirror descent with implicit updates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "user preference learning based edge caching for fog radio access network")
  Member "adaptive bound optimization for online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling adaptive gradient methods from learning rates")

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.DS(Data Structures and Algorithms)