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 "evangelism in social networks algorithms and complexity"):
  Abstract: We consider a population of interconnected individuals that, with respect to a piece of information, at each time instant can be subdivided into three (time-dependent) categories: agnostics, influenced, and evangelists. A dynamical process of information diffusion evolves among the individuals of the population according to the following rules. Initially, all individuals are agnostic. Then, a set of people is chosen from the outside and convinced to start evangelizing, i.e., to start spreading the information. When a number of evangelists, greater than a given threshold, communicate with a node v, the node v becomes influenced, whereas, as soon as the individual v is contacted by a sufficiently much larger number of evangelists, it is itself converted into an evangelist and consequently it starts spreading the information. The question is: How to choose a bounded cardinality initial set of evangelists so as to maximize the final number of influenced individuals? We prove that the problem is hard to solve, even in an approximate sense. On the positive side, we present exact polynomial time algorithms for trees and complete graphs. For general graphs, we derive exact parameterized algorithms. We also investigate the problem when the objective is to select a minimum number of evangelists capable of influencing the whole network. Our motivations to study these problems come from the areas of Viral Marketing and the analysis of quantitative models of spreading of influence in social networks.

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 "on finding small sets that influence large networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "time bounded influence diffusion with incentives")
  Member "contagious sets in dense graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tight bounds on the minimum size of a dynamic monopoly")
  Member "distance constrained labeling on graphs with bounded neighborhood diversity":
    also connected through other hyperedges:
      (none)
  Member "parameterized approximability of maximizing the spread of influence in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "time bounded influence diffusion with incentives")
  Member "using neighborhood diversity to solve hard problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iterated type partitions")

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)