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: "analyzing connectivity of heterogeneous secure sensor networks"
  Abstract: We analyze the connectivity of a heterogeneous secure sensor network that uses key predistribution to protect communications between sensors. For this network on a set   $\mathcal {V}_n$    of   $n$   sensors, suppose there is a pool   $\mathcal {P}_n$   consisting of   $P_n$   distinct keys. The   $n$   sensors in    $\mathcal {V}_n$   are divided into    $m$   groups   $\mathcal {A}_1, \mathcal {A}_2, \ldots, \mathcal {A}_m$  . Each sensor    $v$   is independently assigned to exactly a group according to the probability distribution with   $\mathbb {P}[v \in \mathcal {A}_i]= a_i$    for   $i=1,2,\ldots, m$   , where   $\sum _{i=1}^{m}a_i = 1$   . Afterwards, each sensor in group   $\mathcal {A}_i$    independently chooses   $K_{i,n}$   keys uniformly at random from the key pool   $\mathcal {P}_n$   , where   $K_{1,n} \leq K_{2,n}\leq \ldots \leq K_{m,n}$   . Finally, any two sensors in   $\mathcal {V}_n$    establish a secure link in between if and only if they have at least one key in common. We present critical conditions for connectivity of this heterogeneous secure sensor network. The result provides useful guidelines for the design of secure sensor networks. This paper improves the seminal work  [1]  (IEEE Transactions on Information Theory 2016) of Yagan on connectivity in the following aspects. First, our result is more broadly applicable; specifically, we consider   $K_{m,n} / K_{1,n} = o(\sqrt{n})$  , while  [1]  requires   $K_{m,n} / K_{1,n} = o(\ln n)$  . Put differently,   $K_{m,n} / K_{1,n}$    in our paper examines the case of   $\Theta (n^{x})$   for any   $x     and   $\Theta \big ((\ln n)^y\big)$    for any   $y > 0$  , while that of  [1]  does not cover any   $\Theta (n^{x})$  , and covers   $\Theta \big ((\ln n)^y)$    for only   $0   . This improvement is possible due to a delicate coupling argument. Second, although both studies show that a critical scaling for connectivity is that the term   $b_n$    denoting   $\sum _{j=1}^{m} \left \lbrace a_j \left [1 -{{{P_n-K_{1,n}}\atopwithdelims (){K_{j,n}}}\big /{{P_n}\atopwithdelims (){K_{j,n}}}}\right] \right\rbrace$    equals   $\frac{\ln n}{n}$  , our paper considers any of   $b_{n}=o\big (\frac{ \ln n }{n}\big)$   ,   $b_{n}=\Theta \big (\frac{ \ln n }{n}\big)$   , and   $b_{n}=\omega \big (\frac{ \ln n }{n}\big)$   , while  [1]  evaluates only    $b_{n}=\Theta \big (\frac{ \ln n }{n}\big)$  . Third, in terms of characterizing the transitional behavior of connectivity, our scaling    $b_{n}=\frac{ \ln n + \beta _n}{n}$   for a sequence    $\beta _n$   is more fine-grained than the scaling    $b_{n}\sim \frac{c \ln n}{n}$   for a constant    $c \ne 1$   of  [1] . In a nutshell, we add the case of   $c=1$   in   $b_{n}\sim \frac{c \ln n}{n}$  , where the graph can be connected or disconnected asymptotically, depending on the limit of    $\beta _n$  . Finally, although a recent study by Eletreby and Yagan  [2]  uses the fine-grained scaling discussed before for a more complex graph model, their result (just like  [1] ) also demands    $K_{m,n} / K_{1,n} = o(\ln n)$  , which is less general than   $K_{m,n} / K_{1,n} = o(\sqrt{n})$   addressed in this paper.

Connected through hyperedge HE-1 (induced by source paper "transitional behavior of q composite random key graphs with applications to networked control"):
  with members:
    - "k connectivity in random key graphs with unreliable links"
    - "on resilience and connectivity of secure wireless sensor networks under node capture attacks"
    - "topological properties of secure wireless sensor networks under the q composite key predistribution scheme with unreliable links"
    - "on connectivity and robustness in random intersection graphs"

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.DM(Discrete Mathematics)