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: "characterizing the life cycle of online news stories using social media reactions"
  Abstract: This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.

Connected through hyperedge HE-1 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators"):
  with members:
    - "predicting factuality of reporting and bias of news media sources"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "quootstrap scalable unsupervised extraction of quotation speaker pairs from large news corpora via bootstrapping"
    - "fake news detection on social media a data mining perspective"
    - "learning to identify ambiguous and misleading news headlines"
    - "hoaxy a platform for tracking online misinformation"
    - "stance and sentiment in tweets"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-2 (induced by source paper "sentiment dynamics in social media news channels"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "breaking the news first impressions matter on online news"
    - "how community feedback shapes user behavior"
    - "the pulse of news in social media forecasting popularity"
    - "good friends bad news affect and virality in twitter"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-3 (induced by source paper "studying fake news via network analysis detection and mitigation"):
  with members:
    - "finding streams in knowledge graphs to support fact checking"
    - "fake news detection on social media a data mining perspective"
    - "some like it hoax automated fake news detection in social networks"
    - "computational fact checking from knowledge networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "predicting tomorrow s headline using today s twitter deliberations"):
  with members:
    - "learning from the news predicting entity popularity on twitter"
    - "breaking the news first impressions matter on online news"
    - "crowdsourcing a word emotion association lexicon"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"):
  with members:
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "using facebook ads audiences for global lifestyle disease surveillance promises and limitations"
    - "facebook s gender divide"

Connected through hyperedge HE-6 (induced by source paper "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "fake news mitigation via point process based intervention"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-7 (induced by source paper "will this video go viral explaining and predicting the popularity of youtube videos"):
  with members:
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-8 (induced by source paper "to post or not to post using online trends to predict popularity of offline content"):
  with members:
    - "the pulse of news in social media forecasting popularity"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

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)