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: "pricing strategies for viral marketing on social networks"
  Abstract: We study the use of viral marketing strategies on social networks to maximize revenue from the sale of a single product. We propose a model in which the decision of a buyer to buy the product is influenced by friends that own the product and the price at which the product is offered. The influence model we analyze is quite general, naturally extending both the Linear Threshold model and the Independent Cascade model, while also incorporating price information. We consider sales proceeding in a cascading manner through the network, i.e. a buyer is offered the product via recommendations from its neighbors who own the product. In this setting, the seller influences events by offering a cashback to recommenders and by setting prices (via coupons or discounts) for each buyer in the social network. #R##N#Finding a seller strategy which maximizes the expected revenue in this setting turns out to be NP-hard. However, we propose a seller strategy that generates revenue guaranteed to be within a constant factor of the optimal strategy in a wide variety of models. The strategy is based on an influence-and-exploit idea, and it consists of finding the right trade-off at each time step between: generating revenue from the current user versus offering the product for free and using the influence generated from this sale later in the process. We also show how local search can be used to improve the performance of this technique in practice.

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"
    - "profit maximization over social networks"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "gaps in information access in social networks"):
  with members:
    - "racial categories in machine learning"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "minimizing polarization and disagreement in social networks"
    - "equality of opportunity in supervised learning"
    - "a confidence based approach for balancing fairness and accuracy"
    - "reducing controversy by connecting opposing views"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "fairness through awareness"

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"
    - "profit maximization over social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "coupon advertising in online social systems algorithms and sampling techniques"):
  with members:
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"

Connected through hyperedge HE-5 (induced by source paper "mitigating overexposure in viral marketing"):
  with members:
    - "motifs in temporal networks"

Connected through hyperedge HE-6 (induced by source paper "incentivized campaigning in social networks"):
  with members:
    - "campaigning in heterogeneous social networks optimal control of si information epidemics"
    - "optimal control of information epidemics modeled as maki thompson rumors"
    - "how to run a campaign optimal control of sis and sir information epidemics"

Connected through hyperedge HE-7 (induced by source paper "referral embedded provision point mechanisms for crowdfunding of public projects"):
  with members:
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"
    - "time critical social mobilization"

Connected through hyperedge HE-8 (induced by source paper "an effective marketing strategy for revenue maximization with a quantity constraint"):
  with members:
    - "profit maximization over social networks"
    - "on the efficiency of influence and exploit strategies for revenue maximization under positive externalities"

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.DS(Data Structures and Algorithms)