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: "propagation from deceptive news sources who shares how much how evenly and how quickly"
  Abstract: As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media, we analyze 11 million posts and investigate the propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus subpopulation behavior (by demographics) when spreading misinformation, and distinguish between the two types of propagation, i.e., direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.

Connected through hyperedge HE-1 (induced by source paper "ginger cannot cure cancer battling fake health news with a comprehensive data repository"):
  with members:
    - "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "beyond news contents the role of social context for fake news detection"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-2 (induced by source paper "multilingual multimodal digital deception detection and disinformation spread across social platforms"):
  with members:
    - "identifying and understanding user reactions to deceptive and trusted social news sources"
    - "predicting factuality of reporting and bias of news media sources"
    - "how humans versus bots react to deceptive and trusted news sources a case study of active users"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

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)