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: "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
  Abstract: In this paper, we present a dataset of 713k articles collected between 02/2018-11/2018. These articles are collected directly from 194 news and media outlets including mainstream, hyper-partisan, and conspiracy sources. We incorporate ground truth ratings of the sources from 8 different assessment sites covering multiple dimensions of veracity, including reliability, bias, transparency, adherence to journalistic standards, and consumer trust. The NELA-GT-2018 dataset can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "ginger cannot cure cancer battling fake health news with a comprehensive data repository"):
  with members:
    - "propagation from deceptive news sources who shares how much how evenly and how quickly"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "beyond news contents the role of social context for fake news detection"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-2 (induced by source paper "the pushshift telegram dataset"):
  with members:
    - "can whatsapp counter misinformation by limiting message forwarding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "whatsapp doc a first look at whatsapp public group data"

Connected through hyperedge HE-3 (induced by source paper "nela gt 2019 a large multi labelled news dataset for the study of misinformation in news articles"):
  with members:
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-4 (induced by source paper "r fakeddit a new multimodal benchmark dataset for fine grained fake news detection"):
  with members:
    - "defending against neural fake news"
    - "vatex a large scale high quality multilingual dataset for video and language research"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-5 (induced by source paper "trustworthy misinformation mitigation with soft information nudging"):
  with members:
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "different spirals of sameness a study of content sharing in mainstream and alternative media"
    - "auditing radicalization pathways on youtube"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "predicting factuality of reporting and bias of news media sources"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "csi a hybrid deep model for fake news detection"
    - "node2vec scalable feature learning for networks"

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)