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: "traveling trends social butterflies or frequent fliers"
  Abstract: Trending topics are the online conversations that grab collective attention on social media. They are continually changing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act as sources of traffic and information flow. Taken independently, trends and geography have been discussed in recent literature on online social media; although, so far, little has been done to characterize the relation between trends and geography. Here we investigate more than eleven thousand topics that trended on Twitter in 63 main US locations during a period of 50 days in 2013. This data allows us to study the origins and pathways of trends, how they compete for popularity at the local level to emerge as winners at the country level, and what dynamics underlie their production and consumption in different geographic areas. We identify two main classes of trending topics: those that surface locally, coinciding with three different geographic clusters (East coast, Midwest and Southwest); and those that emerge globally from several metropolitan areas, coinciding with the major air traffic hubs of the country. These hubs act as trendsetters, generating topics that eventually trend at the country level, and driving the conversation across the country. This poses an intriguing conjecture, drawing a parallel between the spread of information and diseases: Do trends travel faster by airplane than over the Internet?

Connected through hyperedge HE-1 (induced by source paper "hashtag usage in a geographically local microblogging app"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "epidemiological modeling of online social network dynamics"

Connected through hyperedge HE-2 (induced by source paper "early detection of promoted campaigns on social media"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "do cascades recur"
    - "the rise of social bots"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-3 (induced by source paper "latent space model for multi modal social data"):
  with members:
    - "structure and inference in annotated networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "scalable text and link analysis with mixed topic link models"

Connected through hyperedge HE-4 (induced by source paper "social politics agenda setting and political communication on social media"):
  with members:
    - "will sanders supporters jump ship for trump fine grained analysis of twitter followers"
    - "the rise of social bots"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"

Connected through hyperedge HE-5 (induced by source paper "style in the age of instagram predicting success within the fashion industry using social media"):
  with members:
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "clustering memes in social media streams"
    - "clustering memes in social media"
    - "trends in social media persistence and decay"

Connected through hyperedge HE-6 (induced by source paper "quantifying the effect of sentiment on information diffusion in social media"):
  with members:
    - "the rise of social bots"
    - "measuring emotional contagion in social media"
    - "on predictability of rare events leveraging social media a machine learning perspective"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "can cascades be predicted"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "predicting successful memes using network and community structure"

Connected through hyperedge HE-7 (induced by source paper "measuring emotional contagion in social media"):
  with members:
    - "on predictability of rare events leveraging social media a machine learning perspective"
    - "the simple rules of social contagion"
    - "quantifying the effect of sentiment on information diffusion in social media"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "can cascades be predicted"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "the digital evolution of occupy wall street"

Connected through hyperedge HE-8 (induced by source paper "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"):
  with members:
    - "the rise of social bots"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "clustering memes in social media streams"
    - "predicting successful memes using network and community structure"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"
    - "clustering memes in social media"

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)