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: "a bayesian approach for predicting the popularity of tweets"
  Abstract: We predict the popularity of short messages called tweets created in the micro-blogging site known as Twitter. We measure the popularity of a tweet by the time-series path of its retweets, which is when people forward the tweet to others. We develop a probabilistic model for the evolution of the retweets using a Bayesian approach, and form predictions using only observations on the retweet times and the local network or "graph" structure of the retweeters. We obtain good step ahead forecasts and predictions of the final total number of retweets even when only a small fraction (i.e., less than one tenth) of the retweet path is observed. This translates to good predictions within a few minutes of a tweet being posted, and has potential implications for understanding the spread of broader ideas, memes, or trends in social networks.

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"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "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 "the trumpiest trump identifying a subject s most characteristic tweets"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "author identification using multi headed recurrent neural networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-3 (induced by source paper "models for predicting community specific interest in news articles"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "talking to the crowd what do people react to in online discussions"

Connected through hyperedge HE-4 (induced by source paper "multi source social feedback of online news feeds"):
  with members:
    - "predictability of popularity gaps between prediction and understanding"
    - "the pulse of news in social media forecasting popularity"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"
    - "analysis of social voting patterns on digg"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "examining a hate speech corpus for hate speech detection and popularity prediction"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "one step and two step classification for abusive language detection on twitter"
    - "analyzing the targets of hate in online social media"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"

Connected through hyperedge HE-6 (induced by source paper "the impact of crowds on news engagement a reddit case study"):
  with members:
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "characterizing the language of online communities and its relation to community reception"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-7 (induced by source paper "on the role of conductance geography and topology in predicting hashtag virality"):
  with members:
    - "can cascades be predicted"
    - "predicting successful memes using network and community structure"
    - "exploring image virality in google plus"
    - "virality prediction and community structure in social networks"
    - "exploring text virality in social networks"
    - "topic diffusion and emergence of virality in social networks"
    - "information diffusion and external influence in networks"
    - "good friends bad news affect and virality in twitter"

Connected through hyperedge HE-8 (induced by source paper "seismic a self exciting point process model for predicting tweet popularity"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling and predicting popularity dynamics via reinforced poisson processes"
    - "can cascades be predicted"
    - "modeling information propagation with survival theory"
    - "the pulse of news in social media forecasting popularity"
    - "structure and dynamics of information pathways in online media"
    - "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)