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 "holistic influence maximization combining scalability and efficiency with opinion aware models"):
  Abstract: The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.

Includes members:
  Member "imrank influence maximization via finding self consistent ranking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rcelf a residual based approach for influence maximization problem")
  Member "influence maximization near optimal time complexity meets practical efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time constrained adaptive influence maximization")
  Member "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs")
  Member "viral marketing meets social advertising ad allocation with minimum regret":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximation algorithms for coordinating ad campaigns on social networks")
  Member "sketch based influence maximization and computation scaling up with guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rcelf a residual based approach for influence maximization problem")
  Member "staticgreedy solving the scalability accuracy dilemma in influence maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")
  Member "maximizing social influence in nearly optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time constrained adaptive influence maximization")
  Member "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)