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: "facebook s advertising platform new attack vectors and the need for interventions"
  Abstract: Ad targeting is getting more powerful with introduction of new tools, such as Custom Audiences, behavioral targeting, and Audience Insights. Although this is beneficial for businesses as it enables people to receive more relevant advertising, the power of the tools has downsides. In this paper, we focus on three downsides: privacy violations, microtargeting (i.e., the ability to reach a specific individual or individuals without their explicit knowledge that they are the only ones an ad reaches) and ease of reaching marginalized groups. Using Facebook's ad system as a case study, we demonstrate the feasibility of such downsides. We then discuss Facebook's response to our responsible disclosures of the findings and call for additional policy, science, and engineering work to protect consumers in the rapidly evolving ecosystem of ad targeting.

Connected through hyperedge HE-1 (induced by source paper "tracking and improving information in the service of fairness"):
  with members:
    - "algorithmic fairness"
    - "50 years of test un fairness lessons for machine learning"
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the price of fair pca one extra dimension"
    - "access to population level signaling as a source of inequality"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-2 (induced by source paper "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"):
  with members:
    - "quantifying the impact of user attention on fair group representation in ranked lists"
    - "preference informed fairness"
    - "fairness under composition"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "myadchoices bringing transparency and control to online advertising"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "xray enhancing the web s transparency with differential correlation"

Connected through hyperedge HE-3 (induced by source paper "ad delivery algorithms the hidden arbiters of political messaging"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "an analysis of united states online political advertising transparency"
    - "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

Connected through hyperedge HE-4 (induced by source paper "technology enabled disinformation summary lessons and recommendations"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "endorsements on social media an empirical study of affiliate marketing disclosures on youtube and pinterest"
    - "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.CY(Computers and Society)