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 "spectral sparsification and regret minimization beyond matrix multiplicative updates"):
  Abstract: In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. #R##N#The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14].

Includes members:
  Member "sparse sums of positive semidefinite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spectral approach to network design")
  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-2 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")
  Member "an efficient algorithm for unweighted spectral graph sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a spectral approach to network design")
  Member "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "an efficient parallel solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "near optimal algorithms for online matrix prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "a matrix hyperbolic cosine algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")

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)