human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "vertex connectivity measures for node failure identification in boolean network tomography"):
  Abstract: We study a few problems in Boolean Network Tomography, related to maximal vertex identifiability, i.e. the maximal number of failing nodes simultaneously identifiable in a network. First, we further our understanding of the relationship between identifiability and $\kappa(G)$, the vertex connectivity of the host network $G$. Second, we look at tradeoffs between the number of monitors and the maximal number of identifiable failures in various classes of random networks. We show that the maximal identifiability of so called Line-of-Sight networks is exactly equal to $\kappa(G)-1$. We then give a tight characterization of the maximal identifiability of any graph $G$ on $n$ nodes with vertex connectivity $\kappa(G) \in \{2, \ldots, n/3\}$. Using Menger's theorem, we prove that the maximal identifiability is always either $\kappa(G)-2$ or $\kappa(G)-1$. Finally we initiate the study of maximal identifiability for random networks. We focus on two models: the classical Erdős-Renyi model, and that of Random Regular graphs. The framework proposed in the paper allows a probabilistic analysis of the identifiability in random networks giving a tradeoff between the number of monitors to place and the maximal identifiability.

Includes members:
  Member "tight bounds for maximal identifiability of failure nodes in boolean network tomography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural network tomography")
  Member "graph constrained group testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for generalized group testing with inhibitors and its potential application in neuroscience")

Question: Please tell me which class the source paper 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)