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: "exploring networks with traceroute like probes theory and simulations"
  Abstract: Mapping the Internet generally consists in sampling the network from a limited set of sources by using traceroute-like probes. This methodology, akin to the merging of different spanning trees to a set of destination, has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. In this paper, we explore these biases and provide a statistical analysis of their origin. We derive an analytical approximation for the probability of edge and vertex detection that exploits the role of the number of sources and targets and allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular, we find that the edge and vertex detection probability depends on the betweenness centrality of each element. This allows us to show that shortest path routed sampling provides a better characterization of underlying graphs with broad distributions of connectivity. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in network models with different topologies. We show that sampled graphs provide a fair qualitative characterization of the statistical properties of the original networks in a fair range of different strategies and exploration parameters. Moreover, we characterize the level of redundancy and completeness of the exploration process as a function of the topological properties of the network. Finally, we study numerically how the fraction of vertices and edges discovered in the sampled graph depends on the particular deployements of probing sources. The results might hint the steps toward more efficient mapping strategies.

Connected through hyperedge HE-1 (induced by source paper "router level community structure of the internet autonomous systems"):
  with members:
    - "uncovering the small community structure in large networks a local spectral approach"
    - "graph spectra and the detectability of community structure in networks"
    - "on weakly optimal partitions in modular networks"
    - "multi level algorithms for modularity clustering"

Connected through hyperedge HE-2 (induced by source paper "a new intrinsic way to measure ixp performance an experience in bolivia"):
  with members:
    - "dimes let the internet measure itself"

Connected through hyperedge HE-3 (induced by source paper "near linear query complexity for graph inference"):
  with members:
    - "graph reconstruction via distance oracles"
    - "topology discovery of sparse random graphs with few participants"
    - "efficient measurement of complex networks using link queries"

Connected through hyperedge HE-4 (induced by source paper "weighted shortest path models a revisit to the simulation of internet routing"):
  with members:
    - "what is the real size of a sampled network the case of the internet"
    - "describing and simulating internet routes"

Connected through hyperedge HE-5 (induced by source paper "algorithmic perspectives of network transitive reduction problems and their applications to synthesis and analysis of biological networks"):
  with members:
    - "on strongly connected digraphs with bounded cycle length"
    - "approximating the minimum equivalent digraph"

Connected through hyperedge HE-6 (induced by source paper "graph reconstruction via distance oracles"):
  with members:
    - "topology discovery of sparse random graphs with few participants"
    - "efficient measurement of complex networks using link queries"

Connected through hyperedge HE-7 (induced by source paper "understanding edge connectivity in the internet through core decomposition"):
  with members:
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition a tool for the visualization of large scale networks"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-8 (induced by source paper "misleading stars what cannot be measured in the internet"):
  with members:
    - "topology discovery of sparse random graphs with few participants"

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)