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 "on connectivity in a general random intersection graph"):
  Abstract: There has been growing interest in studies of general random intersection graphs. In this paper, we consider a general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ defined on a set $\mathcal{V}_n$ comprising $n$ vertices, where $\overrightarrow{a}$ is a probability vector $(a_1,a_2,\ldots,a_m)$ and $\overrightarrow{K_n}$ is $(K_{1,n},K_{2,n},\ldots,K_{m,n})$. This graph has been studied in the literature including a most recent work by Ya\u{g}an [arXiv:1508.02407]. Suppose there is a pool $\mathcal{P}_n$ consisting of $P_n$ distinct objects. The $n$ vertices in $\mathcal{V}_n$ are divided into $m$ groups $\mathcal{A}_1, \mathcal{A}_2, \ldots, \mathcal{A}_m$. Each vertex $v$ is independently assigned to exactly a group according to the probability distribution with $\mathbb{P}[v \in \mathcal{A}_i]= a_i$, where $i=1,2,\ldots,m$. Afterwards, each vertex in group $\mathcal{A}_i$ independently chooses $K_{i,n}$ objects uniformly at random from the object pool $\mathcal{P}_n$. Finally, an undirected edge is drawn between two vertices in $\mathcal{V}_n$ that share at least one object. This graph model $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ has applications in secure sensor networks and social networks. We investigate connectivity in this general random intersection graph $\mathbb{G}(n,\overrightarrow{a}, \overrightarrow{K_n},P_n)$ and present a sharp zero-one law. Our result is also compared with the zero-one law established by Ya\u{g}an.

Includes members:
  Member "k connectivity in random key graphs with unreliable links":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "connectivity in secure wireless sensor networks under transmission constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure communication in dynamic wireless ad hoc networks")
  Member "on the strengths of connectivity and robustness in general random intersection graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k connectivity in random key graphs with unreliable links")
  Member "component evolution in general random intersection graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k connectivity of random graphs and random geometric graphs in node fault model")

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.DM(Discrete Mathematics)