human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "influence maximization meets efficiency and effectiveness a hop based approach"
  Abstract: Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "rcelf a residual based approach for influence maximization problem"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-3 (induced by source paper "efficient approximation algorithms for adaptive target profit maximization"):
  with members:
    - "efficient approximation algorithms for adaptive seed minimization"
    - "coupon advertising in online social systems algorithms and sampling techniques"
    - "towards profit maximization for online social network providers"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "seed selection and social coupon allocation for redemption maximization in online social networks"):
  with members:
    - "stochastic coupon probing in social networks"
    - "towards profit maximization for online social network providers"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "when social influence meets item inference"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "a data based approach to social influence maximization"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "towards profit maximization for online social network providers"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "profit maximization over social networks"
    - "maximizing social influence in nearly optimal time"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "time critical influence maximization in social networks with time delayed diffusion process"
    - "uncovering the temporal dynamics of diffusion networks"
    - "a data based approach to social influence maximization"

Question: Please tell me which class the center node 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)