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: "using a model of social dynamics to predict popularity of news"
  Abstract: Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both companies that host social media sites and their users. Accurate and timely prediction would enable the companies to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions among content quality, how the social media site chooses to highlight content, and influence among users. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating aspects of the web site design, such models improve on predictions based on simply extrapolating from the early votes. We validate this claim on the social news portal Digg using a previously-developed model of social voting based on the Digg user interface.

Connected through hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning"):
  with members:
    - "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "arming the public with artificial intelligence to counter social bots"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "characterizing the 2016 russian ira influence campaign"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"

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"
    - "the pulse of news in social media forecasting popularity"
    - "good friends bad news affect and virality in twitter"
    - "predicting the popularity of online content"

Connected through hyperedge HE-3 (induced by source paper "newscompare a novel application for detecting news influence in a country"):
  with members:
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"

Connected through hyperedge HE-4 (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-5 (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"
    - "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"
    - "predicting the popularity of online content"
    - "analysis of social voting patterns on digg"
    - "novelty and collective attention"

Connected through hyperedge HE-6 (induced by source paper "limiting the spread of fake news on social media platforms by evaluating users trustworthiness"):
  with members:
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "tracking influential individuals in dynamic networks"
    - "computational fact checking from knowledge networks"
    - "events and controversies influences of a shocking news event on information seeking"
    - "tweetcred real time credibility assessment of content on twitter"
    - "processing social media messages in mass emergency a survey"

Connected through hyperedge HE-7 (induced by source paper "multilingual clustering of streaming news"):
  with members:
    - "massively multilingual word embeddings"
    - "unsupervised deep embedding for clustering analysis"
    - "news across languages cross lingual document similarity and event tracking"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "an experimental study of cryptocurrency market dynamics"):
  with members:
    - "bots as virtual confederates design and ethics"
    - "botivist calling volunteers to action using online bots"
    - "social signals and algorithmic trading of bitcoin"
    - "designing and deploying online field experiments"

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)