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: "a stochastic performance model for pipelined krylov methods"
  Abstract: Pipelined Krylov methods seek to ameliorate the latency due to inner products necessary for projection by overlapping it with the computation associated with sparse matrix-vector multiplication. We clarify a folk theorem that this can only result in a speedup of $2\times$ over the naive implementation. Examining many repeated runs, we show that stochastic noise also contributes to the latency, and we model this using an analytical probability distribution. Our analysis shows that speedups greater than $2\times$ are possible with these algorithms.

Connected through hyperedge HE-1 (induced by source paper "comparative study of finite element methods using the time accuracy size tas spectrum analysis"):
  with members:
    - "a scalable variational inequality approach for flow through porous media models with pressure dependent viscosity"
    - "a parallel edge orientation algorithm for quadrilateral meshes"
    - "flexible scalable mesh and data management using petsc dmplex"
    - "mesh algorithms for pde with sieve i mesh distribution"

Connected through hyperedge HE-2 (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"
    - "the communication hiding pipelined bicgstab method for the parallel solution of large unsymmetric linear systems"

Connected through hyperedge HE-3 (induced by source paper "numerical analysis of the maximal attainable accuracy in communication hiding pipelined conjugate gradient methods"):
  with members:
    - "the communication hiding conjugate gradient method with deep pipelines"

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)