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 "scalable influence maximization with general marketing strategies"):
  Abstract: In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$. The IM-GMS problem is to find the best strategy mix $\mathbf{x}^*$ that maximize the influence spread due to influence propagation from the activated seeds, subject to the budget constraint that $\sum_{j\in [d]} x_j \le k$. We adapt the scalable reverse influence sampling (RIS) approach and design a scalable algorithm that provides a $(1-1/e -\varepsilon)$ approximate solution (for any $\varepsilon > 0$), with running time near-linear in the network size. We further extend IM-GMS to allow partitioned budget constraint, and show that our scalable algorithm provides a $(1/2-\varepsilon)$ solution in this case. Through extensive experiments, we demonstrate that our algorithm is several orders faster than the Monte Carlo simulation based hill-climbing algorithm, and also outperforms other baseline algorithms proposed in the literature.

Includes members:
  Member "stochastic submodular maximization the case of coverage functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning and optimization on graphs")
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "guaranteed non convex optimization submodular maximization over continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online policies for efficient volunteer crowdsourcing")

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.SI(Social and Information Networks)