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: "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
  Abstract: With the growing shift towards news consumption primarily through social media sites like Twitter, most of the traditional as well as new-age media houses are promoting their news stories by tweeting about them. The competition for user attention in such mediums has led many media houses to use catchy sensational form of tweets to attract more users - a process known as clickbaiting. In this work, using an extensive dataset collected from Twitter, we analyze the social sharing patterns of clickbait and non-clickbait tweets to determine the organic reach of such tweets. We also attempt to study the sections of Twitter users who actively engage themselves in following clickbait and non-clickbait tweets. Comparing the advent of clickbaits with the rise of tabloidization of news, we bring out several important insights regarding the news consumers as well as the media organizations promoting news stories on Twitter.

Connected through hyperedge HE-1 (induced by source paper "fake news early detection a theory driven model"):
  with members:
    - "xfake explainable fake news detector with visualizations"
    - "open issues in combating fake news interpretability as an opportunity"
    - "fake news detection on social media using geometric deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "a survey on natural language processing for fake news detection"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "automatic detection of fake news"

Connected through hyperedge HE-2 (induced by source paper "federated hierarchical hybrid networks for clickbait detection"):
  with members:
    - "federated reinforcement learning"
    - "federated machine learning concept and applications"
    - "clickbait detection in tweets using self attentive network"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "bag of tricks for efficient text classification"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-3 (induced by source paper "public sphere 2 0 targeted commenting in online news media"):
  with members:
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "using neural network for identifying clickbaits in online news media"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "identifying clickbait a multi strategy approach using neural networks"
    - "clickbait detection in tweets using self attentive network"
    - "a neural clickbait detection engine"
    - "identifying clickbait posts on social media with an ensemble of linear models"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"
    - "fishing for clickbaits in social images and texts with linguistically infused neural network models"

Connected through hyperedge HE-5 (induced by source paper "clickbait detection in tweets using self attentive network"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "a structured self attentive sentence embedding"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "detecting clickbait in online social media you won t believe how we did it"):
  with members:
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "some like it hoax automated fake news detection in social networks"
    - "stop clickbait detecting and preventing clickbaits in online news media"
    - "botornot a system to evaluate social bots"

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)