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: "viral marketing meets social advertising ad allocation with minimum regret"
  Abstract: In this paper, we study the problem of allocating ads to users through the viral-marketing lens. Advertisers approach the host with a budget in return for the marketing campaign service provided by the host. We show that allocation that takes into account the propensity of ads for viral propagation can achieve significantly better performance. However, uncontrolled virality could be undesirable for the host as it creates room for exploitation by the advertisers: hoping to tap uncontrolled virality, an advertiser might declare a lower budget for its marketing campaign, aiming at the same large outcome with a smaller cost. #R##N#This creates a challenging trade-off: on the one hand, the host aims at leveraging virality and the network effect to improve advertising efficacy, while on the other hand the host wants to avoid giving away free service due to uncontrolled virality. We formalize this as the problem of ad allocation with minimum regret, which we show is NP-hard and inapproximable w.r.t. any factor. However, we devise an algorithm that provides approximation guarantees w.r.t. the total budget of all advertisers. We develop a scalable version of our approximation algorithm, which we extensively test on four real-world data sets, confirming that our algorithm delivers high quality solutions, is scalable, and significantly outperforms several natural baselines.

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for coordinating ad campaigns on social networks"):
  with members:
    - "optimizing ad allocation in social advertising"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"
    - "symmetry and approximability of submodular maximization problems"
    - "competitive contagion in networks"
    - "submodular maximization by simulated annealing"
    - "combinatorial auctions with decreasing marginal utilities"

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"
    - "revenue maximization in incentivized social advertising"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "scheduling advertisement delivery in vehicular networks"):
  with members:
    - "what do they know about me contents and concerns of online behavioral profiles"

Connected through hyperedge HE-4 (induced by source paper "revenue maximization in incentivized social advertising"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "how to influence people with partial incentives"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "maximizing social influence in nearly optimal time"
    - "social influence in social advertising evidence from field experiments"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-5 (induced by source paper "holistic influence maximization combining scalability and efficiency with opinion aware models"):
  with members:
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
    - "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"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-6 (induced by source paper "optimizing ad allocation in social advertising"):
  with members:
    - "maximizing social influence in nearly optimal time"
    - "social influence in social advertising evidence from field experiments"
    - "irie scalable and robust influence maximization in 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)