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: "on the dynamics of social media popularity a youtube case study"
  Abstract: Understanding the factors that impact the popularity dynamics of social media can drive the design of effective information services, besides providing valuable insights to content generators and online advertisers. Taking YouTube as case study, we analyze how video popularity evolves since upload, extracting popularity trends that characterize groups of videos. We also analyze the referrers that lead users to videos, correlating them, features of the video and early popularity measures with the popularity trend and total observed popularity the video will experience. Our findings provide fundamental knowledge about popularity dynamics and its implications for services such as advertising and search.

Connected through hyperedge HE-1 (induced by source paper "curating social media data"):
  with members:
    - "how to work a crowd developing crowd capital through crowdsourcing"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "temporal provenance model tpm model and query language"
    - "extending sparql to support entity grouping and path queries"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-2 (induced by source paper "discovering patterns of online popularity from time series"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "dynamical classes of collective attention in twitter"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "novelty and collective attention"

Connected through hyperedge HE-3 (induced by source paper "predicting popularity of online videos using support vector regression"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "deep residual learning for image recognition"
    - "forecasting popularity of videos using social media"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "the pulse of news in social media forecasting popularity"
    - "faster and better a machine learning approach to corner detection"
    - "predicting the popularity of online content"

Connected through hyperedge HE-4 (induced by source paper "understanding the factors that impact the popularity of github repositories"):
  with members:
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-5 (induced by source paper "trendlearner early prediction of popularity trends of user generated content"):
  with members:
    - "improving the effectiveness of content popularity prediction methods using time series trends"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "deriving latent social impulses to determine longevous videos"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

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)