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: "d iteration evaluation of a dynamic partition strategy"
  Abstract: The aim of this paper is to present a first evaluation of a dynamic partition strategy associated to the recently proposed asynchronous distributed computation scheme based on the D-iteration approach. The D-iteration is a fluid diffusion point of view based iteration method to solve numerically linear equations. Using a simple static partition strategy, it has been shown that, when the computation is distributed over K virtual machines (PIDs), the memory size to be handled by each virtual machine decreases linearly with K and the computation speed increases almost linearly with K with a slope becoming closer to one when the number N of linear equations to be solved increases. Here, we want to evaluate how further those results can be improved when a simple dynamic partition strategy is deployed and to show that the dynamic partition strategy allows one to control and equalize the computation load between PIDs without any deep analysis of the matrix or of the underlying graph structure.

Connected through hyperedge HE-1 (induced by source paper "introducing one step back iterative approach to solve linear and non linear fixed point problem"):
  with members:
    - "optimized on line computation of pagerank algorithm"
    - "revisiting the d iteration method from theoretical to practical computation cost"

Connected through hyperedge HE-2 (induced by source paper "convergence of the d iteration algorithm convergence rate and asynchronous distributed scheme"):
  with members:
    - "optimized on line computation of pagerank algorithm"

Connected through hyperedge HE-3 (induced by source paper "understanding differential equations through diffusion point of view non symmetric discrete equations"):
  with members:
    - "optimized on line computation of pagerank algorithm"

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)