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: "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"
  Abstract: A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false-information campaigns with targeted manipulation of public opinion on specific topics. These false-information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one and needs increased public awareness as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community.#R##N##R##N#In this article, we make a step in this direction by providing a typology of the Web’s false-information ecosystem, composed of various types of false-information, actors, and their motives. We report a comprehensive overview of existing research on the false-information ecosystem by identifying several lines of work: (1) how the public perceives false information; (2) understanding the propagation of false information; (3) detecting and containing false information on the Web; and (4) false information on the political stage. In this work, we pay particular attention to political false information as: (1) it can have dire consequences to the community (e.g., when election results are mutated) and (2) previous work shows that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false-information dissemination on the Web.

Connected through hyperedge HE-1 (induced by source paper "combating fake news a survey on identification and mitigation techniques"):
  with members:
    - "adversarial classification on social networks"
    - "all in one multi task learning for rumour verification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "me my echo chamber and i introspection on social media polarization"
    - "fake news detection in social networks via crowd signals"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-2 (induced by source paper "an emotional analysis of false information in social media and news articles"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fully automated fact checking using external sources"
    - "csi a hybrid deep model for fake news detection"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "empath understanding topic signals in large scale text"
    - "feed forward networks with attention can solve some long term memory problems"

Connected through hyperedge HE-3 (induced by source paper "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"):
  with members:
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "predicting factuality of reporting and bias of news media sources"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "false information on web and social media a survey"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-4 (induced by source paper "different spirals of sameness a study of content sharing in mainstream and alternative media"):
  with members:
    - "predicting factuality of reporting and bias of news media sources"
    - "an exploration of verbatim content republishing by news producers"
    - "the spread of low credibility content by social bots"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "the spread of fake news by social bots"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-5 (induced by source paper "performance analysis of online social platforms"):
  with members:
    - "can cascades be predicted"

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)