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: "the pulse of news in social media forecasting popularity"
  Abstract: News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.

Connected through hyperedge HE-1 (induced by source paper "modeling the popularity of twitter hashtags with master equations"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "modeling the infectiousness of twitter hashtags"
    - "virality prediction and community structure in social networks"
    - "stochastic models of user contributory web sites"
    - "novelty and collective attention"

Connected through hyperedge HE-2 (induced by source paper "sentiment dynamics in social media news channels"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "breaking the news first impressions matter on online news"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "how community feedback shapes user behavior"
    - "good friends bad news affect and virality in twitter"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-3 (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"
    - "a bayesian approach for predicting the popularity of tweets"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-4 (induced by source paper "sensing social media signals for cryptocurrency news"):
  with members:
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "cohesiveness in financial news and its relation to market volatility"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "identifying the social signals that drive online discussions a case study of reddit communities"

Connected through hyperedge HE-6 (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-7 (induced by source paper "mining and forecasting career trajectories of music artists"):
  with members:
    - "discovering hidden structure in high dimensional human behavioral data via tensor factorization"
    - "performance dynamics and success in online games"
    - "birank towards ranking on bipartite graphs"
    - "style in the age of instagram predicting success within the fashion industry using social media"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "deepwalk online learning of social representations"
    - "online popularity and topical interests through the lens of instagram"

Connected through hyperedge HE-8 (induced by source paper "multi source social feedback of online news feeds"):
  with members:
    - "predictability of popularity gaps between prediction and understanding"
    - "a bayesian approach for predicting the popularity of tweets"
    - "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"

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.CY(Computers and Society)