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: "revealing the autonomous system taxonomy the machine learning approach"
  Abstract: Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or small private business, university, with vastly different network characteristics, external connectivity patterns, network growth tendencies, and other properties that we can hardly neglect while working on veracious Internet representations in simulation environments. In this paper, we introduce a radically new approach based on machine learning techniques to map all the ASes in the Internet into a natural AS taxonomy. We successfully classify 95.3% of ASes with expected accuracy of 78.1%. We release to the community the AS-level topology dataset augmented with: 1) the AS taxonomy information and 2) the set of AS attributes we used to classify ASes. We believe that this dataset will serve as an invaluable addition to further understanding of the structure and evolution of the Internet.

Connected through hyperedge HE-1 (induced by source paper "rotten apples or bad harvest what we are measuring when we are measuring abuse"):
  with members:
    - "developing security reputation metrics for hosting providers"

Connected through hyperedge HE-2 (induced by source paper "topological trends of internet content providers"):
  with members:
    - "a model of internet topology using k shell decomposition"
    - "as relationships inference and validation"
    - "the internet as level topology three data sources and one definitive metric"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-3 (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"
    - "understanding the evolution dynamics of internet topology"
    - "the rich club phenomenon in the internet topology"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-4 (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"
    - "understanding the evolution dynamics of internet topology"

Connected through hyperedge HE-5 (induced by source paper "graph annotations in modeling complex network topologies"):
  with members:
    - "on compact routing for the internet"
    - "as relationships inference and validation"
    - "systematic topology analysis and generation using degree correlations"
    - "the internet as level topology three data sources and one definitive metric"
    - "exploring networks with traceroute like probes theory and simulations"
    - "inferring as relationships dead end or lively beginning"

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)