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: "endorsements on social media an empirical study of affiliate marketing disclosures on youtube and pinterest"
  Abstract: Online advertisements that masquerade as non-advertising content pose numerous risks to users. Such hidden advertisements appear on social media platforms when content creators or "influencers" endorse products and brands in their content. While the Federal Trade Commission (FTC) requires content creators to disclose their endorsements in order to prevent deception and harm to users, we do not know whether and how content creators comply with the FTC's guidelines. In this paper, we studied disclosures within affiliate marketing, an endorsement-based advertising strategy used by social media content creators. We examined whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosures help users identify affiliate marketing content as advertisements. To do so, we first measured the prevalence of and identified the types of disclosures in over 500,000 YouTube videos and 2.1 million Pinterest pins. We then conducted a user study with 1,791 participants to test the efficacy of these disclosures. Our findings reveal that only about 10% of affiliate marketing content on both platforms contains any disclosures at all. Further, users fail to understand shorter, non-explanatory disclosures. Based on our findings, we make various design and policy suggestions to help improve advertising disclosure practices on social media platforms.

Connected through hyperedge HE-1 (induced by source paper "supporting early and scalable discovery of disinformation websites"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fever a large scale dataset for fact extraction and verification"
    - "trends in the diffusion of misinformation on social media"
    - "fake news detection on social media a data mining perspective"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-2 (induced by source paper "dark patterns at scale findings from a crawl of 11k shopping websites"):
  with members:
    - "a long way to the top significance structure and stability of internet top lists"

Connected through hyperedge HE-3 (induced by source paper "technology enabled disinformation summary lessons and recommendations"):
  with members:
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "detection and resolution of rumours in social media a survey"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "automatic detection of fake news"

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.HC(Human-Computer Interaction)