human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "the spread of low credibility content by social bots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "contagion dynamics of extremist propaganda in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predictive situation awareness for ebola virus disease using a collective intelligence multi model integration platform bayes cloud")
  Member "the spread of fake news by social bots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "disinformation and social bot operations in the run up to the 2017 french presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "rating effects on social news posts and comments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "characterizing behavioral trends in a community driven discussion platform")
  Member "the rise of social bots":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "quantifying the effect of sentiment on information diffusion in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bots elections and social media a brief overview")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")

Question: Please tell me which class the source paper 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)