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 "the strength of connectivity of random graphs induced by pairwise key predistribution schemes implications on security and reliability of heterogeneous sensor networks"):
  Abstract: Random key predistribution schemes have emerged as a widely adopted solution for facilitating secure communication in Wireless Sensor Networks (WSNs). Emerging real world networks increasingly rely on integrating information from sensor nodes with different resources and requirements. We analyze the strength of connectivity of a heterogeneous WSN under the random pairwise key predistribution scheme of Chan et al. According to this scheme, each of the $n$ sensor nodes is classified as type-1 (respectively, type-2) with probability $\mu$ (respectively, $1-\mu)$ where $0<\mu<1$. Each type-1 (respectively, type-2) node is paired with 1 (respectively, $K_n$) other node selected uniformly at random; each pair is then assigned a unique pairwise key so that they can securely communicate with each other. A main question in the design of secure networks is how should the parameters $n$, $\mu$, and $K_n$ be selected such that the resulting network exhibits certain desirable properties with high probability. It was recently established that achieving even 1-connectivity a.a.s. requires $K_n=\omega(1)$ for the inhomogeneous random K-out graph model naturally induced under the heterogeneous pairwise scheme. In this paper, we analyse $k$-connectivity, the property that the network remains connected despite the removal of any $k-1$ nodes or links. First, we establish a sharp zero-one law for $k$-connectivity, showing that for the network to be $k$-connected a.a.s., we need to set $K_n = \frac{1}{1-\mu}(\log n +(k-2)\log\log n + \omega(1))$ for all $k=2, 3, \ldots$. Despite such large scaling of $K_n$ being required for $k$-connectivity, we also show that merely having $K_n =3$ ensures that all but finitely many nodes will form a connected sub-network a.a.s.. We provide comprehensive simulations to demonstrate the usefulness of our results in choosing network parameters in the finite node regime.

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 "towards k connectivity in heterogeneous sensor networks under pairwise key predistribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "high throughput cryptocurrency routing in payment channel networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "k connectivity of inhomogeneous random key graphs with unreliable links":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "toward k connectivity of the random graph induced by a pairwise key predistribution scheme with unreliable links":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "robustness of complex networks with implications for consensus and contagion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effects of topology knowledge and relay depth on asynchronous consensus")

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.IT(Information Theory)