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: "more voices than ever quantifying media bias in networks"
  Abstract: Social media, such as blogs, are often seen as democratic entities that allow more voices to be heard than the conventional mass or elite media. Some also feel that social media exhibits a balancing force against the arguably slanted elite media. A systematic comparison between social and mainstream media is necessary but challenging due to the scale and dynamic nature of modern communication. Here we propose empirical measures to quantify the extent and dynamics of social (blog) and mainstream (news) media bias. We focus on a particular form of bias---coverage quantity---as applied to stories about the 111th US Congress. We compare observed coverage of Members of Congress against a null model of unbiased coverage, testing for biases with respect to political party, popular front runners, regions of the country, and more. Our measures suggest distinct characteristics in news and blog media. A simple generative model, in agreement with data, reveals differences in the process of coverage selection between the two media.

Connected through hyperedge HE-1 (induced by source paper "sampling the news producers a large news and feature data set for the study of the complex media landscape"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "acquiring background knowledge to improve moral value prediction"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "automatically identifying fake news in popular twitter threads"
    - "exploring the ideological nature of journalists social networks on twitter and associations with news story content"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-2 (induced by source paper "you are no jack kennedy on media selection of highlights from presidential debates"):
  with members:
    - "talk it up or play it down un expected correlations between de emphasis and recurrence of discussion points in consequential u s economic policy meetings"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "quotus the structure of political media coverage as revealed by quoting patterns"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "you had me at hello how phrasing affects memorability"

Connected through hyperedge HE-3 (induced by source paper "selection bias in news coverage learning it fighting it"):
  with members:
    - "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
    - "a tutorial on principal component analysis"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "temporal pattern of online communication spike trains in spreading a scientific rumor how often who interacts with whom"):
  with members:
    - "the majority illusion in social networks"
    - "local variation of hashtag spike trains and popularity in twitter"
    - "local variation of collective attention in hashtag spike trains"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "online popularity and topical interests through the lens of instagram"
    - "modelling of trends in twitter using retweet graph dynamics"
    - "the bursty dynamics of the twitter information network"
    - "attention competition with advertisement"

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)