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 "discovering small target sets in social networks a fast and effective algorithm"):
  Abstract: Given a network represented by a graph $G=(V,E)$, we consider a dynamical process of influence diffusion in $G$ that evolves as follows: Initially only the nodes of a given $S\subseteq V$ are influenced; subsequently, at each round, the set of influenced nodes is augmented by all the nodes in the network that have a sufficiently large number of already influenced neighbors. The question is to determine a small subset of nodes $S$ (\emph{a target set}) that can influence the whole network. This is a widely studied problem that abstracts many phenomena in the social, economic, biological, and physical sciences. It is known that the above optimization problem is hard to approximate within a factor of $2^{\log^{1-\epsilon}|V|}$, for any $\epsilon >0$. In this paper, we present a fast and surprisingly simple algorithm that exhibits the following features: 1) when applied to trees, cycles, or complete graphs, it always produces an optimal solution (i.e, a minimum size target set); 2) when applied to arbitrary networks, it always produces a solution of cardinality which improves on the previously known upper bound; 3) when applied to real-life networks, it always produces solutions that substantially outperform the ones obtained by previously published algorithms (for which no proof of optimality or performance guarantee is known in any class of graphs).

Includes members:
  Member "on finding small sets that influence large networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time bounded influence diffusion with incentives")
  Member "spread of influence in weighted networks under time and budget constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time bounded influence diffusion with incentives")
  Member "parameterized approximability of maximizing the spread of influence in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized approximation schemes for independent set of rectangles and geometric knapsack")
  Member "latency bounded target set selection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time bounded influence diffusion with incentives")
  Member "a scalable heuristic for viral marketing under the tipping model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "influence maximization for fixed heterogeneous thresholds")
  Member "influence diffusion in social networks under time window constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time bounded influence diffusion with incentives")
  Member "contagious sets in expanders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time bounded influence diffusion with incentives")
  Member "target set selection problem for honeycomb networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.DS(Data Structures and Algorithms)