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: "lessons from three views of the internet topology"
  Abstract: Network topology plays a vital role in understanding the performance of network applications and protocols. Thus, recently there has been tremendous interest in generating realistic network topologies. Such work must begin with an understanding of existing network topologies, which today typically consists of a relatively small number of data sources. In this paper, we calculate an extensive set of important characteristics of Internet AS-level topologies extracted from the three data sources most frequently used by the research community: traceroutes, BGP, and WHOIS. We find that traceroute and BGP topologies are similar to one another but differ substantially from the WHOIS topology. We discuss the interplay between the properties of the data sources that result from specific data collection mechanisms and the resulting topology views. We find that, among metrics widely considered, the joint degree distribution appears to fundamentally characterize Internet AS-topologies: it narrowly defines values for other important metrics. We also introduce an evaluation criteria for the accuracy of topology generators and verify previous observations that generators solely reproducing degree distributions cannot capture the full spectrum of critical topological characteristics of any of the three topologies. Finally, we release to the community the input topology datasets, along with the scripts and output of our calculations. This supplement should enable researchers to validate their models against real data and to make more informed selection of topology data sources for their specific needs.

Connected through hyperedge HE-1 (induced by source paper "performance of local information based link prediction a sampling perspective"):
  with members:
    - "link prediction in complex networks a clustering perspective"
    - "towards unbiased bfs sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "time series of internet as level topology graphs four patterns and one model"):
  with members:
    - "graph annotations in modeling complex network topologies"
    - "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"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-3 (induced by source paper "radial structure of the internet"):
  with members:
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-4 (induced by source paper "k core decomposition of internet graphs hierarchies self similarity and measurement biases"):
  with members:
    - "a model of internet topology using k shell decomposition"
    - "what is the real size of a sampled network the case of the internet"
    - "systematic topology analysis and generation using degree correlations"
    - "exploring networks with traceroute like probes theory and simulations"
    - "k core decomposition a tool for the visualization of large scale networks"
    - "dimes let the internet measure itself"
    - "an o m algorithm for cores decomposition of networks"

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)