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: "accurately modeling the internet topology"
  Abstract: Based on measurements of the internet topology data, we found that there are two mechanisms which are necessary for the correct modeling of the internet topology at the autonomous systems (AS) level: the interactive growth of new nodes and new internal links, and a nonlinear preferential attachment, where the preference probability is described by a positive-feedback mechanism. Based on the above mechanisms, we introduce the positive-feedback preference (PFP) model which accurately reproduces many topological properties of the AS-level internet, including degree distribution, rich-club connectivity, the maximum degree, shortest path length, short cycles, disassortative mixing, and betweenness centrality. The PFP model is a phenomenological model which provides an insight into the evolutionary dynamics of real complex networks.

Connected through hyperedge HE-1 (induced by source paper "disentangling interpretable generative parameters of random and real world graphs"):
  with members:
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning deep generative models of graphs"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "on rich clubs of path based centralities in networks"):
  with members:
    - "locally estimating core numbers"
    - "truss decomposition in massive networks"
    - "core periphery structure in networks"
    - "multiscale network generation"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-3 (induced by source paper "rich club ordering and the dyadic effect two interrelated phenomena"):
  with members:
    - "exploring biological network structure with clustered random networks"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-4 (induced by source paper "the thin bridges of as connectivity measuring dependency using as hegemony"):
  with members:
    - "nation state routing censorship wiretapping and bgp"
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-5 (induced by source paper "internet topology over time"):
  with members:
    - "modeling internet scale policies for cleaning up malware"
    - "beyond node degree evaluating as topology models"
    - "the internet as level topology three data sources and one definitive metric"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-6 (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"
    - "revealing the autonomous system taxonomy the machine learning approach"
    - "the rich club phenomenon in the internet topology"

Connected through hyperedge HE-7 (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-8 (induced by source paper "review a critical look at power law modelling of the internet"):
  with members:
    - "beyond node degree evaluating as topology models"
    - "chinese internet as level topology"
    - "a model of internet topology using k shell decomposition"
    - "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"
    - "exploring networks with traceroute like probes theory and simulations"
    - "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)