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: "optimizing ad allocation in social advertising"
  Abstract: Social advertising (or social promotion) is an effective approach that produces a significant cascade of adoption through influence in the online social networks. The goal of this work is to optimize the ad allocation from the platform's perspective. On the one hand, the platform would like to maximize revenue earned from each advertiser by exposing their ads to as many people as possible, one the other hand, the platform wants to reduce free-riding to ensure the truthfulness of the advertiser. To access this tradeoff, we adopt the concept of \emph{regret} \citep{viral2015social} to measure the performance of an ad allocation scheme. In particular, we study two social advertising problems: \emph{budgeted social advertising problem} and \emph{unconstrained social advertising problem}. In the first problem, we aim at selecting a set of seeds for each advertiser that minimizes the regret while setting budget constraints on the attention cost; in the second problem, we propose to optimize a linear combination of the regret and attention costs. We prove that both problems are NP-hard, and then develop a constant factor approximation algorithm for each problem.

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for coordinating ad campaigns on social networks"):
  with members:
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "viral marketing meets social advertising ad allocation with minimum regret"
    - "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 "stochastic coupon probing in social networks"):
  with members:
    - "no time to observe adaptive influence maximization with partial feedback"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "submodular stochastic probing on matroids"
    - "a stochastic probing problem with applications"
    - "maximizing social influence in nearly optimal time"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "no time to observe adaptive influence maximization with partial feedback"):
  with members:
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "irie scalable and robust influence maximization in social networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)