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: "questioning the assumptions behind fairness solutions"
  Abstract: In addition to their benefits, optimization systems can have negative economic, moral, social, and political effects on populations as well as their environments. Frameworks like fairness have been proposed to aid service providers in addressing subsequent bias and discrimination during data collection and algorithm design. However, recent reports of neglect, unresponsiveness, and malevolence cast doubt on whether service providers can effectively implement fairness solutions. These reports invite us to revisit assumptions made about the service providers in fairness solutions. Namely, that service providers have (i) the incentives or (ii) the means to mitigate optimization externalities. Moreover, the environmental impact of these systems suggests that we need (iii) novel frameworks that consider systems other than algorithmic decision-making and recommender systems, and (iv) solutions that go beyond removing related algorithmic biases. Going forward, we propose Protective Optimization Technologies that enable optimization subjects to defend against negative consequences of optimization systems.

Connected through hyperedge HE-1 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"):
  with members:
    - "ethically aligned design of autonomous systems industry viewpoint and an empirical study"
    - "model cards for model reporting"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "the global landscape of ai ethics guidelines"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "manipulating and measuring model interpretability"
    - "the dataset nutrition label a framework to drive higher data quality standards"

Connected through hyperedge HE-3 (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"
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "algorithms that don t see color comparing biases in lookalike and special ad audiences"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "ad delivery algorithms the hidden arbiters of political messaging"
    - "fairness under composition"
    - "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-5 (induced by source paper "human self determination within algorithmic sociotechnical systems"):
  with members:
    - "model cards for model reporting"
    - "delayed impact of fair machine learning"
    - "improving recommender systems beyond the algorithm"
    - "bias disparity in recommendation systems"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "what does it mean to solve the problem of discrimination in hiring social technical and legal perspectives from the uk on automated hiring systems"):
  with members:
    - "mitigating bias in algorithmic hiring evaluating claims and practices"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

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)