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 "nearly linear time packing and covering lp solvers"):
  Abstract: Packing and covering linear programs (PC-LPs) form an important class of linear programs (LPs) across computer science, operations research, and optimization. In 1993, Luby and Nisan constructed an iterative algorithm for approximately solving PC-LPs in nearly linear time, where the time complexity scales nearly linearly in $N$, the number of nonzero entries of the matrix, and polynomially in $\varepsilon$, the (multiplicative) approximation error. Unfortunately, all existing nearly linear-time algorithms for solving PC-LPs require time at least proportional to $\varepsilon^{-2}$. #R##N#In this paper, we break this longstanding barrier by designing a packing solver that runs in time $\tilde{O}(N \varepsilon^{-1})$ and covering LP solver that runs in time $\tilde{O}(N \varepsilon^{-1.5})$. Our packing solver can be extended to run in time $\tilde{O}(N \varepsilon^{-1})$ for a class of well-behaved covering programs. In a follow-up work, Wang et al. showed that all covering LPs can be converted into well-behaved ones by a reduction that blows up the problem size only logarithmically. #R##N#At high level, these two algorithms can be described as linear couplings of several first-order descent steps. This is an application of our linear coupling technique to problems that are not amenable to blackbox applications known iterative algorithms in convex optimization.

Includes members:
  Member "a nearly linear time ptas for explicit fractional packing and covering linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "sequential and parallel algorithms for mixed packing and covering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")
  Member "on the number of iterations for dantzig wolfe optimization and packing covering approximation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate degree secret sharing and concentration phenomena")

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)