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 "faster parallel solver for positive linear programs via dynamically bucketed selective coordinate descent"):
  Abstract: We provide improved parallel approximation algorithms for the important class of packing and covering linear programs. In particular, we present new parallel $\epsilon$-approximate packing and covering solvers which run in $\tilde{O}(1/\epsilon^2)$ expected time, i.e., in expectation they take $\tilde{O}(1/\epsilon^2)$ iterations and they do $\tilde{O}(N/\epsilon^2)$ total work, where $N$ is the size of the constraint matrix and $\epsilon$ is the error parameter, and where the $\tilde{O}$ hides logarithmic factors. To achieve our improvement, we introduce an algorithmic technique of broader interest: dynamically-bucketed selective coordinate descent (DB-SCD). At each step of the iterative optimization algorithm, the DB-SCD method dynamically buckets the coordinates of the gradient into those of roughly equal magnitude, and it updates all the coordinates in one of the buckets. This dynamically-bucketed updating permits us to take steps along several coordinates with similar-sized gradients, thereby permitting more appropriate step sizes at each step of the algorithm. In particular, this technique allows us to use in a straightforward manner the recent analysis from the breakthrough results of Allen-Zhu and Orecchia [2] to achieve our still-further improved bounds. More generally, this method addresses "interference" among coordinates, by which we mean the impact of the update of one coordinate on the gradients of other coordinates. Such interference is a core issue in parallelizing optimization routines that rely on smoothness properties. Since our DB-SCD method reduces interference via updating a selective subset of variables at each iteration, we expect it may also have more general applicability in optimization.

Includes members:
  Member "using optimization to obtain a width independent parallel simpler and faster positive sdp solver":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "unified acceleration method for packing and covering problems via diameter reduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating the held karp bound for metric tsp in nearly linear time")
  Member "linear coupling an ultimate unification of gradient and mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "a nearly linear time ptas for explicit fractional packing and covering linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  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-5 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")
  Member "nearly linear time approximation schemes for mixed packing covering and facility location linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "parallel coordinate descent for l1 regularized loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning systems for highly distributed and rapidly growing data")

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)