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: "what is the real size of a sampled network the case of the internet"
  Abstract: Internet mapping projects generally consist in sampling the network from a limited set of sources by usingtraceroute probes. This methodology, akin to the merging of spanning trees from the different sources to a set of destinations, leads necessarily to a partial, incomplete map of the Internet. Accordingly, determination of Internet topology characteristics from such sampled maps is in part a problem of statistical inference. Our contribution begins with the observation that the inference of many of the most basic topological quantities ‐ including network size and degree characteristics ‐ fromtraceroute measurements is in fact a version of the so-called ‘species problem’ in statistics. This obse rvation has important implications, since species problems are often quite challenging. We focus here on the most fundamental example of atraceroute internet species: the number of nodes in a network. Specifica lly,

Connected through hyperedge HE-1 (induced by source paper "inferring structural characteristics of networks with strong and weak ties from fixed choice surveys"):
  with members:
    - "the role of social networks in information diffusion"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "social contagion theory examining dynamic social networks and human behavior"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-2 (induced by source paper "assessing the effectiveness of real world network simplification"):
  with members:
    - "a new algorithm for extracting a small representative subgraph from a very large graph"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "weighted shortest path models a revisit to the simulation of internet routing"):
  with members:
    - "exploring networks with traceroute like probes theory and simulations"
    - "describing and simulating internet routes"

Connected through hyperedge HE-4 (induced by source paper "graph size estimation"):
  with members:
    - "social structure of facebook networks"
    - "coarse grained topology estimation via graph sampling"
    - "on the bias of bfs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "the workshop on internet topology wit report"):
  with members:
    - "chinese internet as level topology"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "bounding the bias of tree like sampling in ip topologies"):
  with members:
    - "exploring networks with traceroute like probes theory and simulations"
    - "a geographic directed preferential internet topology model"
    - "dimes let the internet measure itself"

Connected through hyperedge HE-7 (induced by source paper "measuring fundamental properties of real world complex networks"):
  with members:
    - "theory and practice of triangle problems in very large sparse power law graphs"
    - "fast approximation of centrality"

Connected through hyperedge HE-8 (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"
    - "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"
    - "lessons from three views of the internet topology"
    - "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)