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: "breaking the news first impressions matter on online news"
  Abstract: A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.

Connected through hyperedge HE-1 (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"
    - "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"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-2 (induced by source paper "learning to determine the quality of news headlines"):
  with members:
    - "using neural network for identifying clickbaits in online news media"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "text matching as image recognition"
    - "generating news headlines with recurrent neural networks"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "predicting tomorrow s headline using today s twitter deliberations"):
  with members:
    - "learning from the news predicting entity popularity on twitter"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "crowdsourcing a word emotion association lexicon"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "supporting the use of user generated content in journalistic practice"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "using gaussian processes for rumour stance classification in social media"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "stance detection with bidirectional conditional encoding"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"

Connected through hyperedge HE-5 (induced by source paper "detecting incongruity between news headline and body text via a deep hierarchical encoder"):
  with members:
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "sampling the news producers a large news and feature data set for the study of the complex media landscape"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "identifying the social signals that drive online discussions a case study of reddit communities"
    - "acquiring background knowledge to improve moral value prediction"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "automatically identifying fake news in popular twitter threads"
    - "exploring the ideological nature of journalists social networks on twitter and associations with news story content"
    - "hoaxy a platform for tracking online misinformation"

Connected through hyperedge HE-7 (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"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "talking to the crowd what do people react to in online discussions"
    - "a bayesian approach for predicting the popularity of tweets"

Connected through hyperedge HE-8 (induced by source paper "inside the right leaning echo chambers characterizing gab an unmoderated social system"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "white man and highly followed gender and race inequalities in twitter"
    - "fake news detection on social media a data mining perspective"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "analyzing the targets of hate in online social media"

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)