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 "a threshold for clusters in real world random networks"):
  Abstract: Recent empirical work [Leskovec2009] has suggested the existence of a size threshold for the existence of clusters within many real-world networks. We give the first proof that this clustering size threshold exists within a real-world random network model, and determine the asymptotic value at which it occurs. #R##N#More precisely, we choose the Community Guided Attachment (CGA) random network model of Leskovek, Kleinberg, and Faloutsos [Leskovec2005]. The model is non-uniform and contains self-similar communities, and has been shown to have many properties of real-world networks. To capture the notion of clustering, we follow Mishra et. al. [Mishra2007], who defined a type of clustering for real-world networks: an (\alpha,\beta)-cluster is a set that is both internally dense (to the extent given by the parameter \beta), and externally sparse (to the extent given by the parameter \alpha) . With this definition of clustering, we show the existence of a size threshold of (\ln n)^{1/2} for the existence of clusters in the CGA model. For all \epsilon>0, a.a.s. clusters larger than (\ln n)^{1/2-\epsilon} exist, whereas a.a.s. clusters larger than (\ln n)^{1/2+\epsilon} do not exist. Moreover, we show a size bound on the existence of small, constant-size clusters.

Includes members:
  Member "multiplicative attribute graph model of real world networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph generators state of the art and open challenges")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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.SI(Social and Information Networks)