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 web aggregation approach for distributed randomized pagerank algorithms"
  Abstract: The PageRank algorithm employed at Google assigns a measure of importance to each web page for rankings in search results. In our recent papers, we have proposed a distributed randomized approach for this algorithm, where web pages are treated as agents computing their own PageRank by communicating with linked pages. This paper builds upon this approach to reduce the computation and communication loads for the algorithms. In particular, we develop a method to systematically aggregate the web pages into groups by exploiting the sparsity inherent in the web. For each group, an aggregated PageRank value is computed, which can then be distributed among the group members. We provide a distributed update scheme for the aggregated PageRank along with an analysis on its convergence properties. The method is especially motivated by results on singular perturbation techniques for large-scale Markov chains and multi-agent consensus. A numerical example is provided to illustrate the level of reduction in computation while keeping the error in rankings small.

Connected through hyperedge HE-1 (induced by source paper "ergodic randomized algorithms and dynamics over networks"):
  with members:
    - "pagerank optimization by edge selection"
    - "almost sure convergence of a randomized algorithm for relative localization in sensor networks"
    - "convergence of distributed randomized pagerank algorithms"
    - "gossips and prejudices ergodic randomized dynamics in social networks"
    - "limited benefit of cooperation in distributed relative localization"
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "error scaling laws for linear optimal estimation from relative measurements"

Connected through hyperedge HE-2 (induced by source paper "the pagerank problem multi agent consensus and web aggregation a systems and control viewpoint"):
  with members:
    - "pagerank optimization by edge selection"
    - "convergence of distributed randomized pagerank algorithms"
    - "distributed randomized algorithms for the pagerank computation"
    - "pagerank standing on the shoulders of giants"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-3 (induced by source paper "almost sure convergence of a randomized algorithm for relative localization in sensor networks"):
  with members:
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "error scaling laws for linear optimal estimation from relative measurements"

Connected through hyperedge HE-4 (induced by source paper "convergence of distributed randomized pagerank algorithms"):
  with members:
    - "distributed randomized algorithms for the pagerank computation"
    - "asynchronous iterative computations with web information retrieval structures the pagerank case"

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.SY(Systems and Control)