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 "optimizing spread of influence in weighted social networks via partial incentives"):
  Abstract: A widely studied process of influence diffusion in social networks posits that the dynamics of influence diffusion evolves as follows: Given a graph $G=(V,E)$, representing the network, initially \emph{only} the members 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 general problem is to find a small initial set of nodes that influences the whole network. In this paper we extend the previously described basic model in the following ways: firstly, we assume that there are non negative values $c(v)$ associated to each node $v\in V$, measuring how much it costs to initially influence node $v$, and the algorithmic problem is to find a set of nodes of \emph{minimum total cost} that influences the whole network; successively, we study the consequences of giving \emph{incentives} to member of the networks, and we quantify how this affects (i.e., reduces) the total costs of starting process that influences the whole network. For the two above problems we provide both hardness and algorithmic results. We also experimentally validate our algorithms via extensive simulations on real life networks.

Includes members:
  Member "contagious sets in dense graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight bounds on the minimum size of a dynamic monopoly")
  Member "latency bounded target set selection in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time bounded influence diffusion with incentives")
  Member "contagious sets in expanders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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)