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: "measuring and optimizing cultural markets"
  Abstract: In their seminal paper [16], Salganik, Dodds, and Watts introduced an artificial music market (the MusicLab) to study why experts routinely fail at predicting commercial success of cultural products. The MusicLab was instrumental in demonstrating that social influence can create significant unpredictability in cultural markets. Watts [23] also advocates the use of “measure and react” strategies to counteract the diculty of making accurate predictions. However, finding a concrete strategy that scales for very large markets has remained elusive so far. This paper demonstrates the benefits of such a strategy in the MusicLab setting: It presents a “measure and react” strategy, called “measure and optimize”, that combines songs quality, appeal, and social influence to maximize the expected downloads at each iteration. Computational experiments show that our “measure and optimize” strategy can leverage social influence to produce significant performance benefits for the market. Moreover, this paper formally proves that a “measure and optimize” strategy with social influence outperforms in expectation any “measure and react” strategy not displaying social information. In other words, when using a “measure and optimize” strategy, dynamically showing consumers positive social information increases the expected performance of the seller in the cultural market.

Connected through hyperedge HE-1 (induced by source paper "assortment optimization under a multinomial logit model with position bias and social influence"):
  with members:
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"

Connected through hyperedge HE-2 (induced by source paper "popularity and quality in social news aggregators a study of reddit and hacker news"):
  with members:
    - "can cascades be predicted"
    - "the pulse of news in social media forecasting popularity"
    - "the role of social networks in information diffusion"
    - "using a model of social dynamics to predict popularity of news"
    - "stochastic models of user contributory web sites"

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)