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: "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
  Abstract: The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this paper, we demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the "relevance" of ads to different groups of users. We find that both the advertiser's budget and the content of the ad each significantly contribute to the skew of Facebook's ad delivery. Critically, we observe significant skew in delivery along gender and racial lines for "real" ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves---and not just the targeting choices of advertisers---in preventing discrimination in digital advertising.

Connected through hyperedge HE-1 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "adversarial attacks on copyright detection systems"
    - "explaining explanations in ai"
    - "delayed impact of fair machine learning"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poisoning attacks to graph based recommender systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "questioning the assumptions behind fairness solutions"

Connected through hyperedge HE-2 (induced by source paper "roles for computing in social change"):
  with members:
    - "what is the point of fairness disability ai and the complexity of justice"
    - "mitigating bias in algorithmic hiring evaluating claims and practices"
    - "model cards for model reporting"
    - "on the legal compatibility of fairness definitions"
    - "problem formulation and fairness"
    - "data science as political action grounding data science in a politics of justice"
    - "using search queries to understand health information needs in africa"
    - "datasheets for datasets"

Connected through hyperedge HE-3 (induced by source paper "awareness in practice tensions in access to sensitive attribute data for antidiscrimination"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "problem formulation and fairness"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "an intersectional definition of fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "the cost of fairness in classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-4 (induced by source paper "preference informed fairness"):
  with members:
    - "individual fairness in sponsored search auctions"
    - "eliciting and enforcing subjective individual fairness"
    - "envy free classification"
    - "online learning with an unknown fairness metric"
    - "fairness through computationally bounded awareness"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibration for the computationally identifiable masses"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-5 (induced by source paper "toward controlling discrimination in online ad auctions"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "reducing controversy by connecting opposing views"
    - "discrimination in online ad delivery"
    - "bidding for representative allocations for display advertising"

Connected through hyperedge HE-6 (induced by source paper "algorithms that don t see color comparing biases in lookalike and special ad audiences"):
  with members:
    - "ad delivery algorithms the hidden arbiters of political messaging"
    - "fairness under composition"
    - "questioning the assumptions behind fairness solutions"
    - "hunting for discriminatory proxies in linear regression models"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-7 (induced by source paper "multi category fairness in sponsored search auctions"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "balanced news using constrained bandit based personalization"
    - "a new system wide diversity measure for recommendations with efficient algorithms"
    - "fairness under composition"
    - "ranking with fairness constraints"
    - "fairness through awareness"

Connected through hyperedge HE-8 (induced by source paper "ad delivery algorithms the hidden arbiters of political messaging"):
  with members:
    - "an analysis of united states online political advertising transparency"
    - "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"

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)