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: "inapproximability and parameterized results for the target set selection problem"
  Abstract: Given an undirected graph $G$ with a threshold function $\tau:V(G) \rightarrow \mathbb{N}$, the \emph{Target Set Selection (TSS) Problem} is about choosing a minimum cardinality set, say $S \subseteq V(G)$, such that starting a diffusion process with $S$ as its seed set will eventually result in activating all the nodes in $G$. Under the non-progressive diffusion model, we have the following results on the TSS Problem:  We show that the TSS Problem on bipartite graphs does not admit an approximation algorithm with a performance guarantee asymptotically better than $O(\log n_{min})$, where $n_{min}$ is the cardinality of the smaller bipartition, unless $P=NP$. Though Chen et al. [SIDMA, 2009] had shown an inapproximability result of $\Omega(2^{\log^{1 - \epsilon} n})$ for the TSS Problem on general graphs under the progressive diffusion model, to the best of our knowledge, our result is the first inapproximability result under the non-progressive diffusion setting.  Assuming a non-progressive diffusion model, it was shown by Nichterlein et al. [Social Network Analysis and Mining, 2013] that it is possible to compute an optimal sized target set in $O(2^{(2^{t}+1)t}\cdot m)$ time, where $m$ and $t$ denote the number of edges and the cardinality of a minimum vertex cover, respectively, of the graph under consideration. We improve this result by designing an algorithm that computes an optimal sized target set in $2^{O(t\log t)}n^{O(1)}$ time, where $n$ denotes the number of vertices of the graph under consideration.

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.CC(Computational Complexity)