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 "on finding small sets that influence large networks"):
  Abstract: We consider the problem of selecting a minimum size subset of nodes in a network that allows to activate all the nodes of the network. We present a fast and simple algorithm that, in real-life networks, produces solutions that outperform the ones obtained by using the best algorithms in the literature. We also investigate the theoretical performances of our algorithm and give proofs of optimality for some classes of graphs. From an experimental perspective, experiments also show that the performance of the algorithms correlates with the modularity of the analyzed network. Moreover, the more the influence among communities is hard to propagate, the less the performances of the algorithms differ. On the other hand, when the network allows some propagation of influence between different communities, the gap between the solutions returned by the proposed algorithm and by the previous algorithms in the literature increases.

Includes members:
  Member "spread of influence in weighted networks under time and budget constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time bounded influence diffusion with incentives")
  Member "contagious sets in dense graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tight bounds on the minimum size of a dynamic monopoly")
  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)