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 "beyond worst case in approximability of nonsubmodular influence maximization"):
  Abstract: We consider the problem of maximizing the spread of influence in a social network by choosing a fixed number of initial seeds, formally referred to as the influence maximization problem. It admits a (1−1/e)-factor approximation algorithm if the influence function is submodular. Otherwise, in the worst case, the problem is NP-hard to approximate to within a factor of N1−v. This article studies whether this worst-case hardness result can be circumvented by making assumptions about either the underlying network topology or the cascade model. All our assumptions are motivated by many real-life social network cascades.#R##N##R##N#First, we present strong inapproximability results for a very restricted class of networks called the (stochastic) hierarchical blockmodel, a special case of the well-studied (stochastic) blockmodel in which relationships between blocks admit a tree structure. We also provide a dynamic-programming-based polynomial time algorithm, which optimally computes a directed variant of the influence maximization problem on hierarchical blockmodel networks. Our algorithm indicates that the inapproximability result is due to the bidirectionality of influence between agent-blocks.#R##N##R##N#Second, we present strong inapproximability results for a class of influence functions that are “almost” submodular, called 2-quasi-submodular. Our inapproximability results hold even for any 2-quasi-submodular f fixed in advance. This result also indicates that the “threshold” between submodularity and nonsubmodularity is sharp, regarding the approximability of influence maximization.

Includes members:
  Member "don t be greedy leveraging community structure to find high quality seed sets for influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "robust influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeding with costly network information")
  Member "competitive contagion in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximation algorithms for coordinating ad campaigns on social networks")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")

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)