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: "rising tides or rising stars dynamics of shared attention on twitter during media events"
  Abstract: “Media events” generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. We examine how collective patterns of user behavior under conditions of shared attention are distinct from other “bursts” of activity like breaking news events. Using 290 million tweets from a panel of 193,532 politically active Twitter users, we compare features of their behavior during eight major events during the 2012 U.S. presidential election to examine how patterns of social media use change during these media events compared to “typical” time and whether these changes are attributable to shifts in the behavior of the population as a whole or shifts from particular segments such as elites. Compared to baseline time periods, our findings reveal that media events not only generate large volumes of tweets, but they are also associated with (1) substantial declines in interpersonal communication, (2) more highly concentrated attention by replying to and retweeting particular users, and (3) elite users predominantly benefiting from this attention. These findings empirically demonstrate how bursts of activity on Twitter during media events significantly alter underlying social processes of interpersonal communication and social interaction. Because the behavior of large populations within socio-technical systems can change so dramatically, our findings suggest the need for further research about how social media responses to media events can be used to support collective sensemaking, to promote informed deliberation, and to remain resilient in the face of misinformation.

Connected through hyperedge HE-1 (induced by source paper "a model for meme popularity growth in social networking systems based on biological principle and human interest dynamics"):
  with members:
    - "limited individual attention and online virality of low quality information"
    - "local variation of hashtag spike trains and popularity in twitter"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "a bayesian approach for predicting the popularity of tweets"
    - "dynamical classes of collective attention in twitter"
    - "what trends in chinese social media"
    - "agents bookmarks and clicks a topical model of web navigation"

Connected through hyperedge HE-2 (induced by source paper "characterizing collective attention via descriptor context a case study of public discussions of crisis events"):
  with members:
    - "pomo generating entity specific post modifiers in context"
    - "massively multilingual transfer for ner"
    - "the effect of collective attention on controversial debates on social media"
    - "credibility and dynamics of collective attention"
    - "globally normalized transition based neural networks"
    - "processing social media messages in mass emergency a survey"
    - "dynamical classes of collective attention in twitter"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "interactional and informational attention on twitter"):
  with members:
    - "local variation of hashtag spike trains and popularity in twitter"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "the bursty dynamics of the twitter information network"
    - "virality prediction and community structure in social networks"
    - "dynamical classes of collective attention in twitter"
    - "novelty and collective attention"

Connected through hyperedge HE-4 (induced by source paper "systematically monitoring social media the case of the german federal election 2017"):
  with members:
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "social media monitoring of the campaigns for the 2013 german bundestag elections on facebook and twitter"

Connected through hyperedge HE-5 (induced by source paper "big data challenges for the social sciences from society and opinion to replications"):
  with members:
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "selection and influence in cultural dynamics"
    - "dynamical classes of collective attention in twitter"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-6 (induced by source paper "anticipating activity in social media spikes"):
  with members:
    - "the role of social networks in information diffusion"
    - "social contagion an empirical study of information spread on digg and twitter follower graphs"
    - "dynamical classes of collective attention in twitter"

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)