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: "understanding the evolution dynamics of internet topology"
  Abstract: The internet structure is extremely complex. The positive-feedback preference (PFP) model is a recently introduced internet topology generator. The model uses two generic algorithms to replicate the evolution dynamics observed on the internet historic data. The phenomenological model was originally designed to match only two topology properties of the internet, i.e., the rich-club connectivity and the exact form of degree distribution, whereas numerical evaluation has shown that the PFP model accurately reproduces a large set of other nontrivial characteristics as well. This paper aims to investigate why and how this generative model captures so many diverse properties of the internet. Based on comprehensive simulation results, the paper presents a detailed analysis on the exact origin of each of the topology properties produced by the model. This work reveals how network evolution mechanisms control the obtained topology properties and it also provides insights on correlations between various structural characteristics of complex networks.

Connected through hyperedge HE-1 (induced by source paper "phase changes in the evolution of the ipv4 and ipv6 as level internet topologies"):
  with members:
    - "on cost effective communication network designing"
    - "modeling the ipv6 internet as level topology"
    - "chinese internet as level topology"
    - "the internet as level topology three data sources and one definitive metric"
    - "revealing the autonomous system taxonomy the machine learning approach"
    - "the rich club phenomenon in the internet topology"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-2 (induced by source paper "evolution of the internet as level ecosystem"):
  with members:
    - "graph annotations in modeling complex network topologies"
    - "an integrated model of traffic geography and economy in the internet"
    - "chinese internet as level topology"
    - "as relationships inference and validation"
    - "the workshop on internet topology wit report"
    - "systematic topology analysis and generation using degree correlations"
    - "the internet as level topology three data sources and one definitive metric"
    - "revealing the autonomous system taxonomy the machine learning approach"

Connected through hyperedge HE-3 (induced by source paper "enhancing network transmission capacity by efficiently allocating node capability"):
  with members:
    - "on cost effective communication network designing"
    - "chinese internet as level topology"
    - "a model of internet topology using k shell decomposition"
    - "structural bottlenecks for communication in networks"
    - "the internet as level topology three data sources and one definitive metric"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-4 (induced by source paper "chinese internet as level topology"):
  with members:
    - "the internet as level topology three data sources and one definitive metric"
    - "k core decomposition a tool for the visualization of large scale networks"
    - "the rich club phenomenon in the internet topology"

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.NI(Networking and Internet Architecture)