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: "profit maximization over social networks"
  Abstract: Influence maximization is the problem of finding a set of influential users in a social network such that the expected spread of influence under a certain propagation model is maximized. Much of the previous work has neglected the important distinction between social influence and actual product adoption. However, as recognized in the management science literature, an individual who gets influenced by social acquaintances may not necessarily adopt a product (or technology), due, e.g., to monetary concerns. In this work, we distinguish between influence and adoption by explicitly modeling the states of being influenced and of adopting a product. We extend the classical Linear Threshold (LT) model to incorporate prices and valuations, and factor them into users' decision-making process of adopting a product. We show that the expected profit function under our proposed model maintains submodularity under certain conditions, but no longer exhibits monotonicity, unlike the expected influence spread function. To maximize the expected profit under our extended LT model, we employ an unbudgeted greedy framework to propose three profit maximization algorithms. The results of our detailed experimental study on three real-world datasets demonstrate that of the three algorithms, PAGE, which assigns prices dynamically based on the profit potential of each candidate seed, has the best performance both in the expected profit achieved and in running time.

Connected through hyperedge HE-1 (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"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "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"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-2 (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"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "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"
    - "a data based approach to social influence maximization"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-3 (induced by source paper "a k hop collaborate game model adaptive strategy to maximize total revenue"):
  with members:
    - "adaptive influence maximization with myopic feedback"
    - "adaptive influence maximization under general feedback models"
    - "adaptive submodular influence maximization with myopic feedback"
    - "robust influence maximization"
    - "no time to observe adaptive influence maximization with partial feedback"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-4 (induced by source paper "towards profit maximization for online social network providers"):
  with members:
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an effective marketing strategy for revenue maximization with a quantity constraint"):
  with members:
    - "on the efficiency of influence and exploit strategies for revenue maximization under positive externalities"
    - "pricing strategies for viral marketing on social networks"

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)