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: "local variation of hashtag spike trains and popularity in twitter"
  Abstract: We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.

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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "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 "modelling structure and predicting dynamics of discussion threads in online boards"):
  with members:
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "a likelihood based framework for the analysis of discussion threads"
    - "quantifying long term scientific impact"
    - "from user comments to on line conversations"

Connected through hyperedge HE-3 (induced by source paper "interactional and informational attention on twitter"):
  with members:
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "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 "hashtag usage in a geographically local microblogging app"):
  with members:
    - "epidemiological modeling of online social network dynamics"
    - "traveling trends social butterflies or frequent fliers"

Connected through hyperedge HE-5 (induced by source paper "vcube ps a causal broadcast topic based publish subscribe system"):
  with members:
    - "causal consistency beyond memory"

Connected through hyperedge HE-6 (induced by source paper "sleeping beauties in meme diffusion"):
  with members:
    - "the birth of collective memories analyzing emerging entities in text streams"
    - "wikipedia page view reflects web search trend"
    - "who creates trends in online social media the crowd or opinion leaders"
    - "can cascades be predicted"
    - "predicting successful memes using network and community structure"
    - "virality prediction and community structure in social networks"
    - "how to improve the prediction based on citation impact percentiles for years shortly after the publication date"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-7 (induced by source paper "temporal pattern of online communication spike trains in spreading a scientific rumor how often who interacts with whom"):
  with members:
    - "the majority illusion in social networks"
    - "local variation of collective attention in hashtag spike trains"
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "online popularity and topical interests through the lens of instagram"
    - "modelling of trends in twitter using retweet graph dynamics"
    - "the bursty dynamics of the twitter information network"
    - "attention competition with advertisement"
    - "how random are online social interactions"

Connected through hyperedge HE-8 (induced by source paper "local variation of collective attention in hashtag spike trains"):
  with members:
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "attention competition with advertisement"

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)