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 "whom to befriend to influence people"):
  Abstract: Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person $v$ in the network has a certain threshold $t(v)$ for {\em activation}, i.e adoption of the product or idea. If $v$ has at least $t(v)$ activated neighbors, then $v$ will also become activated. If Alice wants to activate the entire social network, whom should she befriend? More generally, we study the problem of finding the minimum number of links that a set of external influencers should form to people in the network, in order to activate the entire social network. This {\em Minimum Links} Problem has applications in viral marketing and the study of epidemics. Its solution can be quite different from the related and widely studied Target Set Selection problem. We prove that the Minimum Links problem cannot be approximated to within a ratio of $O(2^{\log^{1-\epsilon} n})$, for any fixed $\epsilon>0$, unless $NP\subseteq DTIME(n^{polylog(n)})$, where $n$ is the number of nodes in the network. On the positive side, we give linear time algorithms to solve the problem for trees, cycles, and cliques, for any given set of external influencers, and give precise bounds on the number of links needed. For general graphs, we design a polynomial time algorithm to compute size-efficient link sets that can activate the entire graph.

Includes members:
  Member "selecting nodes and buying links to maximize the information diffusion in a network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time bounded influence diffusion with incentives")
  Member "recommending links through influence maximization":
    also connected through other hyperedges:
      (none)
  Member "discovering small target sets in social networks a fast and effective algorithm":
    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 "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 "how to influence people with partial incentives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable lattice influence maximization")

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)