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: "on the network reliability problem of the heterogeneous key predistribution scheme"
  Abstract: We consider the network reliability problem in wireless sensor networks secured by the heterogeneous random key predistribution scheme. This scheme generalizes Eschenauer-Gligor scheme by considering the cases when the network comprises sensor nodes with varying level of resources; e.g., regular nodes vs. cluster heads. The scheme induces the inhomogeneous random key graph, denoted $\mathbb{G}(n;\pmb{\mu},\pmb{K},P)$. We analyze the reliability of $\mathbb{G}(n;\pmb{\mu},\pmb{K},P)$ against random link failures. Namely, we consider $\mathbb{G}(n;\pmb{\mu},\pmb{K}, P,\alpha)$ formed by deleting each edge of $\mathbb{G}(n;\pmb{\mu},\pmb{K},P)$ independently with probability $1-\alpha$, and study the probability that the resulting graph i) has no isolated node; and ii) is connected. We present scaling conditions on $\pmb{K}$, $P$, and $\alpha$ such that both events take place with probability zero or one, respectively, as the number of nodes gets large. We present numerical results to support these in the finite-node regime.

Connected through hyperedge HE-1 (induced by source paper "secure and reliable connectivity in heterogeneous wireless sensor networks"):
  with members:
    - "k connectivity in random key graphs with unreliable links"
    - "node isolation of secure wireless sensor networks under a heterogeneous channel model"
    - "minimum node degree in inhomogeneous random key graphs with unreliable links"
    - "on the strengths of connectivity and robustness in general random intersection graphs"
    - "performance of the eschenauer gligor key distribution scheme under an on off channel"

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.IT(Information Theory)