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: "information spread over an internet mediated social network phases speed width and effects of promotion"
  Abstract: In this study, we looked at the effect of promotion in the speed and width of spread of information on the Internet by tracking the diffusion of news articles over a social network. Speed of spread means the number of readers that the news has reached in a given time, while width of spread means how far the story has travelled from the news originator within the social network. After analyzing six stories in a 30-hour time span, we found out that the lifetime of a story's popularity among the members of the social network has three phases: Expansion, Front-page, and Saturation. Expansion phase starts when a story is published and the article spreads from a source node to nodes within a connected component of the social network. Front-page phase happens when a news aggregator promotes the story in its front page resulting to the story's faster rate of spread among the connected nodes while at the same time spreading the article to nodes outside the original connected component of the social network. Saturation phase is when the story ages and its rate of spread within the social network slows down, suggesting popularity saturation among the nodes. Within these three phases, we observed minimal changes on the width of information spread as suggested by relatively low increase of the width of the spread's diameter within the social network. We see that this paper provides the various stakeholders a first-hand empirical data for modeling, designing, and improving the current web-based services, specifically the IT educators for designing and improving academic curricula, and for improving the current web-enabled deployment of knowledge and online evaluation of skills.

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)