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: "bieber blast bieberblast early prediction of popular hashtag compounds"
  Abstract: Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.

Connected through hyperedge HE-1 (induced by source paper "competing topic naming conventions in quora predicting appropriate topic merges and winning topics from millions of topic pairs"):
  with members:
    - "universal sentence encoder"
    - "deep dive into anonymity a large scale analysis of quora questions"
    - "language use matters analysis of the linguistic structure of question texts can characterize answerability in quora"
    - "competition and selection among conventions"
    - "analysis of named entity recognition and linking for tweets"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "who is authoritative understanding reputation mechanisms in quora"

Connected through hyperedge HE-2 (induced by source paper "competition and selection among conventions"):
  with members:
    - "the status gradient of trends in social media"
    - "the social dynamics of language change in online networks"
    - "the effects of age gender and region on non standard linguistic variation in online social networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "can cascades be predicted"
    - "multi level modeling of quotation families morphogenesis"
    - "you had me at hello how phrasing affects memorability"
    - "on the interplay between social and topical structure"

Connected through hyperedge HE-3 (induced by source paper "cascades a view from audience"):
  with members:
    - "can cascades be predicted"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "you had me at hello how phrasing affects memorability"
    - "on the interplay between social and topical structure"

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)