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: "automatically identifying fake news in popular twitter threads"
  Abstract: Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or "fake news," present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies. We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists. All three datasets, aligned into a uniform format, are also publicly available. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "dclaims a censorship resistant web annotations system using ipfs and ethereum"):
  with members:
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "bitcoin ng a scalable blockchain protocol"
    - "bitcoin over tor isn t a good idea"

Connected through hyperedge HE-4 (induced by source paper "fake news a survey of research detection methods and opportunities"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "linknbed multi graph representation learning with entity linkage"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "belittling the source trustworthiness indicators to obfuscate fake news on the web"
    - "the spread of low credibility content by social bots"
    - "ti cnn convolutional neural networks for fake news detection"
    - "clickbait detection in tweets using self attentive network"

Connected through hyperedge HE-5 (induced by source paper "a survey on natural language processing for fake news detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "automated fact checking task formulations methods and future directions"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "fever a large scale dataset for fact extraction and verification"
    - "twowingos a two wing optimization strategy for evidential claim verification"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "a stylometric inquiry into hyperpartisan and fake news"

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"
    - "exploring the ideological nature of journalists social networks on twitter and associations with news story content"
    - "hoaxy a platform for tracking online misinformation"
    - "stop clickbait detecting and preventing clickbaits in online news media"

Connected through hyperedge HE-7 (induced by source paper "polarization and fake news early warning of potential misinformation targets"):
  with members:
    - "polarization of the vaccination debate on facebook"
    - "misinformation spreading on facebook"
    - "it s always april fools day on the difficulty of social network misinformation classification via propagation features"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "fake news detection on social media a data mining perspective"
    - "tweet acts a speech act classifier for twitter"
    - "debunking in a world of tribes"
    - "vews a wikipedia vandal early warning system"

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)