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: "connectivity of large scale networks emergence of unique unbounded component"
  Abstract: This paper studies networks where all nodes are distributed on a unit square $A\triangleq[(-1/2,1/2)^{2}$ following a Poisson distribution with known density $\rho$ and a pair of nodes separated by an Euclidean distance $x$ are directly connected with probability $g(\frac{x}{r_{\rho}})$, independent of the event that any other pair of nodes are directly connected. Here $g:[0,\infty)\rightarrow[0,1]$ satisfies the conditions of rotational invariance, non-increasing monotonicity, integral boundedness and $g(x)=o(\frac{1}{x^{2}\log^{2}x})$; further, $r_{\rho}=\sqrt{\frac{\log\rho+b}{C\rho}}$ where $C=\int_{\Re^{2}}g(\Vert \boldsymbol{x}\Vert)d\boldsymbol{x}$ and $b$ is a constant. Denote the above network by\textmd{}$\mathcal{G}(\mathcal{X}_{\rho},g_{r_{\rho}},A)$. We show that as $\rho\rightarrow\infty$, asymptotically almost surely a) there is no component in $\mathcal{G}(\mathcal{X}_{\rho},g_{r_{\rho}},A)$ of fixed and finite order $k>1$; b) the number of components with an unbounded order is one. Therefore as $\rho\rightarrow\infty$, the network asymptotically almost surely contains a unique unbounded component and isolated nodes only; a sufficient condition for $\mathcal{G}(\mathcal{X}_{\rho},g_{r_{\rho}},A)$ to be asymptotically almost surely connected is that there is no isolated node in the network.{\normalsize{}}The contribution of these results, together with results in a companion paper on the asymptotic distribution of isolated nodes in \textmd{\normalsize $\mathcal{G}(\mathcal{X}_{\rho},g_{r_{\rho}},A)$}, is to expand recent results obtained for connectivity of random geometric graphs from the unit disk model to the more generic and more practical random connection model.

Connected through hyperedge HE-1 (induced by source paper "connectivity of confined dense networks boundary effects and scaling laws"):
  with members:
    - "broadcast analysis for large cooperative wireless networks"
    - "towards a better understanding of large scale network models"
    - "extremal properties of three dimensional sensor networks with applications"

Connected through hyperedge HE-2 (induced by source paper "research on wireless multi hop networks current state and challenges"):
  with members:
    - "connectivity of large scale networks distribution of isolated nodes"
    - "towards a better understanding of large scale network models"
    - "extremal properties of three dimensional sensor networks with applications"

Connected through hyperedge HE-3 (induced by source paper "on the quality of wireless network connectivity"):
  with members:
    - "connectivity of large scale networks distribution of isolated nodes"
    - "towards a better understanding of large scale network models"

Connected through hyperedge HE-4 (induced by source paper "connectivity of large scale networks distribution of isolated nodes"):
  with members:
    - "extremal properties of three dimensional sensor networks with applications"

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)