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 "an inapproximability result for the target set selection problem on bipartite graphs"):
  Abstract: Given an undirected graph $\mathcal{G}(V, E, \tau)$ modeling a 'social network', where each node $v$ is associated with a threshold value $\tau(v)$, a set of vertices $\mathcal{S} \subseteq V(\mathcal{G})$ (called 'seed nodes') is chosen initially. Now a 'social contagion process' is defined on $\mathcal{G}$ as follows. At time $t=0$ the nodes in $\mathcal{S}$ have some information, and it is diffused in discrete time steps. An 'un-influenced' node $v$ will change its state to 'influenced' at time step $t$ if it has at least $\tau(v)$ number of neighbors, which are influenced at time step $t-1$. The process continues until no more node activation is possible. Based on this diffusion process, a well-studied problem in the literature is the 'Target Set Selection Problem (TSS Problem)', where the goal is to influence all the nodes of the network by initially choosing the minimum number of seed nodes. Chen et al. [On the Approximability of Influence in Social Networks. SIAM Journal on Discrete Mathematics, 23(3):1400-1415, 2009] showed that the decision version of this optimization problem is NP-Hard on bounded degree bipartite graphs. In this paper, we show that this problem on bipartite graph does not admit an approximation algorithm with a performance guarantee asymptotically better than $\mathcal{O}(\log n_{min})$, where $n_{min}$ is the cardinality of the smaller bipartition, unless $P=NP$.

Includes members:
  Member "target set selection in dense graph classes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iterated type partitions")
  Member "target set selection problem for honeycomb networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inapproximability and parameterized results for the target set selection problem")

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)