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: "revenue maximization in incentivized social advertising"
  Abstract: Incentivized social advertising, an emerging marketing model, provides monetization opportunities not only to the owners of the social networking platforms but also to their influential users by offering a "cut" on the advertising revenue. We consider a social network (the host) that sells ad-engagements to advertisers by inserting their ads, in the form of promoted posts, into the feeds of carefully selected "initial endorsers" or seed users: these users receive monetary incentives in exchange for their endorsements. The endorsements help propagate the ads to the feeds of their followers. In this context, the problem for the host is is to allocate ads to influential users, taking into account the propensity of ads for viral propagation, and carefully apportioning the monetary budget of each of the advertisers between incentives to influential users and ad-engagement costs, with the rational goal of maximizing its own revenue. We consider a monetary incentive for the influential users, which is proportional to their influence potential. We show that revenue maximization in incentivized social advertising corresponds to the problem of monotone submodular function maximization, subject to a partition matroid constraint on the ads-to-seeds allocation, and submodular knapsack constraints on the advertisers' budgets. This problem is NP-hard and we devise 2 greedy algorithms with provable approximation guarantees, which differ in their sensitivity to seed user incentive costs. Our approximation algorithms require repeatedly estimating the expected marginal gain in revenue as well as in advertiser payment. By exploiting a connection to the recent advances made in scalable estimation of expected influence spread, we devise efficient and scalable versions of the greedy algorithms.

Connected through hyperedge HE-1 (induced by source paper "locec local community based edge classification in large online social networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "model slicing for supporting complex analytics with elastic inference cost and resource constraints"
    - "real time influence maximization on dynamic social streams"
    - "xgboost a scalable tree boosting system"
    - "database meets deep learning challenges and opportunities"
    - "fast r cnn"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "social influence in social advertising evidence from field experiments"

Connected through hyperedge HE-2 (induced by source paper "maximizing the diversity of exposure in a social network"):
  with members:
    - "finding low tension communities"
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "reducing controversy by connecting opposing views"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "modeling adoption and usage of competing products"

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)