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: "the communication hiding pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"
  Abstract: Abstract   A High Performance Computing alternative to traditional Krylov subspace methods, pipelined Krylov subspace solvers offer better scalability in the strong scaling limit compared to standard Krylov subspace methods for large and sparse linear systems. The typical synchronization bottleneck is mitigated by overlapping time-consuming global communication phases with local computations in the algorithm. This paper describes a general framework for deriving the pipelined variant of any Krylov subspace algorithm. The proposed framework was implicitly used to derive the pipelined Conjugate Gradient (p-CG) method in  Hiding global synchronization latency in the preconditioned Conjugate Gradient algorithm  by P. Ghysels and W. Vanroose, Parallel Computing, 40(7):224–238, 2014. The pipelining framework is subsequently illustrated by formulating a pipelined version of the BiCGStab method for the solution of large unsymmetric linear systems on parallel hardware. A residual replacement strategy is proposed to account for the possible loss of attainable accuracy and robustness by the pipelined BiCGStab method. It is shown that the pipelined algorithm improves scalability on distributed memory machines, leading to significant speedups compared to standard preconditioned BiCGStab.

Connected through hyperedge HE-1 (induced by source paper "recent developments in iterative methods for reducing synchronization"):
  with members:
    - "asynchronous time parallel method based on laplace transform"
    - "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"
    - "asynchronous communications library for the parallel in time solution of black scholes equation"
    - "asynchronous parareal algorithm applied to european option pricing"
    - "avoiding communication in primal and dual block coordinate descent methods"

Connected through hyperedge HE-2 (induced by source paper "improving strong scaling of the conjugate gradient method for solving large linear systems using global reduction pipelining"):
  with members:
    - "numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method"
    - "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"
    - "the communication hiding conjugate gradient method with deep pipelines"

Connected through hyperedge HE-3 (induced by source paper "numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method"):
  with members:
    - "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"
    - "low synchronization gmres algorithms"
    - "the communication hiding conjugate gradient method with deep pipelines"

Connected through hyperedge HE-4 (induced by source paper "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"):
  with members:
    - "the communication hiding conjugate gradient method with deep pipelines"
    - "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"
    - "a stochastic performance model for pipelined krylov methods"

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)