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: "optimized on line computation of pagerank algorithm"
  Abstract: In this paper we present new ideas to accelerate the computation of the eigenvector of the transition matrix associated to the PageRank algorithm. New ideas are based on the decomposition of the matrix-vector product that can be seen as a fluid diffusion model, associated to new algebraic equations. We show through experiments on synthetic data and on real data-sets how much this approach can improve the computation efficiency.

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:
    - "d iteration evaluation of a dynamic partition strategy"
    - "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:
    - "d iteration evaluation of a dynamic partition strategy"

Connected through hyperedge HE-3 (induced by source paper "d iteration evaluation of a dynamic partition strategy"):
  with members:
    - "asynchronous iterative computations with web information retrieval structures the pagerank case"

Connected through hyperedge HE-4 (induced by source paper "understanding differential equations through diffusion point of view non symmetric discrete equations"):
  with members:
    - "d iteration evaluation of a dynamic partition strategy"

Connected through hyperedge HE-5 (induced by source paper "optimizing the eigenvector computation algorithm with diffusion approach"):
  with members:
    - "revisiting the d iteration method from theoretical to practical computation cost"

Connected through hyperedge HE-6 (induced by source paper "d iteration evaluation of the update algorithm"):
  with members:
    - "d iteration based asynchronous distributed computation"

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.DM(Discrete Mathematics)