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 "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.

Includes members:
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "target set selection problem for honeycomb networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time bounded influence diffusion with incentives")

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.CC(Computational Complexity)