human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rating reliability and bias in news articles does ai assistance help everyone"):
  Abstract: With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participants to understand if algorithmic assistance improves the accuracy of reliability and bias perceptions, and whether there is a difference in the effectiveness of the AI assistance for different types of news consumers. We find that AI assistance with feature-based explanations improves the accuracy of news perceptions. However, some consumers are helped more than others. Specifically, we find that participants who read and share news often on social media are worse at recognizing bias and reliability issues in news articles than those who do not, while frequent news readers and those familiar with politics perform much better. We discuss these differences and their implication to offer insights for future research.

Includes members:
  Member "predicting factuality of reporting and bias of news media sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "sampling the news producers a large news and feature data set for the study of the complex media landscape":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nela gt 2019 a large multi labelled news dataset for the study of misinformation in news articles")
  Member "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "a stylometric inquiry into hyperpartisan and fake news":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hgat hierarchical graph attention network for fake news detection")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "stop clickbait detecting and preventing clickbaits in online news media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the chameleon attack manipulating content display in online social media")
  Member "trend of narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "influence of fake news in twitter during the 2016 us presidential election")
  Member "science vs conspiracy collective narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the covid 19 social media infodemic")

Question: Please tell me which class the source paper 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)