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 conjugate gradient method with deep pipelines"
  Abstract: Krylov subspace methods are among the most efficient present-day solvers for large scale linear algebra problems. Nevertheless, classic Krylov subspace method algorithms do not scale well on massively parallel hardware due to the synchronization bottlenecks induced by the computation of dot products throughout the algorithms. Communication-hiding pipelined Krylov subspace methods offer increased parallel scalability. One of the first published methods in this class is the pipelined Conjugate Gradient method (p-CG), which exhibits increased speedups on parallel machines. This is achieved by overlapping the time-consuming global communication phase with useful (independent) computations such as spmvs, hence reducing the impact of global communication as a synchronization bottleneck and avoiding excessive processor idling. However, on large numbers of processors the time spent in the global communication phase can be much higher than the time required for computing a single spmv. This work extends the pipelined CG method to deeper pipelines, which allows further scaling when the global communication phase is the dominant time-consuming factor. By overlapping the global all-to-all reduction phase in each CG iteration with the next l spmvs (pipelining), the method is able to hide communication latency behind computational work. The derivation of the p(l)-CG algorithm is based on the existing p(l)-GMRES method. Moreover, a number of theoretical and implementation properties of the p(l)-CG method are presented, including a preconditioned version of the algorithm. Experimental results are presented to demonstrate the possible performance gains of using deeper pipelines for solving large scale symmetric linear systems with the new CG method variant.

Connected through hyperedge HE-1 (induced by source paper "scalable resilience against node failures for communication hiding preconditioned conjugate gradient and conjugate residual methods"):
  with members:
    - "improving strong scaling of the conjugate gradient method for solving large linear systems using global reduction pipelining"
    - "how to make the preconditioned conjugate gradient method resilient against multiple node failures"
    - "numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method"
    - "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"
    - "the adaptive s step conjugate gradient method"
    - "numerically stable variants of the communication hiding pipelined conjugate gradients algorithm for the parallel solution of large scale symmetric linear systems"
    - "a backward forward recovery approach for the preconditioned conjugate gradient method"

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 pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"

Connected through hyperedge HE-3 (induced by source paper "new communication hiding conjugate gradient variants"):
  with members:
    - "numerically stable recurrence relations for the communication hiding pipelined conjugate gradient method"
    - "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"
    - "numerically stable variants of the communication hiding pipelined conjugate gradients algorithm for the parallel solution of large scale symmetric linear systems"

Connected through hyperedge HE-4 (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 pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"

Connected through hyperedge HE-5 (induced by source paper "analyzing and improving maximal attainable accuracy in the communication hiding pipelined bicgstab method"):
  with members:
    - "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"
    - "the communication hiding pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"
    - "a stochastic performance model for pipelined krylov methods"

Connected through hyperedge HE-6 (induced by source paper "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"):
  with members:
    - "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)