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: "stop clickbait detecting and preventing clickbaits in online news media"
  Abstract: Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. #R##N#In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.

Connected through hyperedge HE-1 (induced by source paper "the chameleon attack manipulating content display in online social media"):
  with members:
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"

Connected through hyperedge HE-2 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"

Connected through hyperedge HE-3 (induced by source paper "the web of false information rumors fake news hoaxes clickbait and various other shenanigans"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "false information on web and social media a survey"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "fake news mitigation via point process based intervention"

Connected through hyperedge HE-4 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"

Connected through hyperedge HE-5 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-6 (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"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"

Connected through hyperedge HE-7 (induced by source paper "towards reliable online clickbait video detection a content agnostic approach"):
  with members:
    - "the age of social sensing"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "clickbait identification using neural networks"
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (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"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "diving deep into clickbaits who use them to what extents in which topics with what effects"
    - "learning to identify ambiguous and misleading news headlines"
    - "bag of tricks for efficient text classification"
    - "federated optimization distributed machine learning for on device intelligence"

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)