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 "maximizing contrasting opinions in signed social networks"):
  Abstract: The classic influence maximization problem finds a limited number of influential seed users in a social network such that the expected number of influenced users in the network, following an influence cascade model, is maximized. The problem has been studied in different settings, with further generalization of the graph structure, e.g., edge weights and polarities, target user categories, etc. In this paper, we introduce a unique influence diffusion scenario involving a population that split into two distinct groups, with opposing views. We aim at finding the top-$k$ influential seed nodes so to simultaneously maximize the adoption of two distinct, antithetical opinions in the two groups, respectively. Efficiently finding such influential users is essential in a wide range of applications such as increasing voter engagement and turnout, steering public debates and discussions on societal issues with contentious opinions. We formulate this novel problem with the voter model to simulate opinion diffusion and dynamics, and then design a linear-time and exact algorithm COSiNeMax, while also investigating the long-term opinion characteristics in the network. Our experiments with several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm, compared to various baselines.

Includes members:
  Member "the wisdom of polarized crowds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social centralization and semantic collapse hyperbolic embeddings of networks and text")
  Member "minimizing polarization and disagreement in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "choice set optimization under discrete choice models of group decisions")
  Member "reducing controversy by connecting opposing views":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vocabulary based method for quantifying controversy in social media")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "from competition to complementarity comparative influence diffusion and maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback")
  Member "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discovering polarized communities in signed networks")

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)