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 "trustworthy misinformation mitigation with soft information nudging"):
  Abstract: Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. #R##N#We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

Includes members:
  Member "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ginger cannot cure cancer battling fake health news with a comprehensive data repository")
  Member "rating reliability and bias in news articles does ai assistance help everyone":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans")
  Member "different spirals of sameness a study of content sharing in mainstream and alternative media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "safe similarity aware multi modal fake news detection")
  Member "auditing radicalization pathways on youtube":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the incel community on youtube")
  Member "what is gab a bastion of free speech or an alt right echo chamber":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere")
  Member "predicting factuality of reporting and bias of news media sources":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  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-7 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "csi a hybrid deep model for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)