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: "problem formulation and fairness"
  Abstract: Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the identification of appropriate target variables and proxies. While these choices are rarely self-evident, normative assessments of data science projects often take them for granted, even though different translations can raise profoundly different ethical concerns. Whether we consider a data science project fair often has as much to do with the formulation of the problem as any property of the resulting model. Building on six months of ethnographic fieldwork with a corporate data science team---and channeling ideas from sociology and history of science, critical data studies, and early writing on knowledge discovery in databases---we describe the complex set of actors and activities involved in problem formulation. Our research demonstrates that the specification and operationalization of the problem are always negotiated and elastic, and rarely worked out with explicit normative considerations in mind. In so doing, we show that careful accounts of everyday data science work can help us better understand how and why data science problems are posed in certain ways---and why specific formulations prevail in practice, even in the face of what might seem like normatively preferable alternatives. We conclude by discussing the implications of our findings, arguing that effective normative interventions will require attending to the practical work of problem formulation.

Connected through hyperedge HE-1 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-2 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-3 (induced by source paper "measurement and fairness"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "the scientific method in the science of machine learning"
    - "the profiling potential of computer vision and the challenge of computational empiricism"
    - "racial categories in machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "teaching responsible data science charting new pedagogical territory"):
  with members:
    - "a nutritional label for rankings"
    - "datasheets for datasets"
    - "explaining explanations an overview of interpretability of machine learning"
    - "manipulating and measuring model interpretability"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "a unified approach to interpreting model predictions"
    - "measuring fairness in ranked outputs"
    - "on the im possibility of fairness"

Connected through hyperedge HE-6 (induced by source paper "predictive multiplicity in classification"):
  with members:
    - "fairwashing the risk of rationalization"
    - "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
    - "aequitas a bias and fairness audit toolkit"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "how to use heuristics for differential privacy"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-7 (induced by source paper "mitigating bias in algorithmic hiring evaluating claims and practices"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "what does it mean to solve the problem of discrimination in hiring social technical and legal perspectives from the uk on automated hiring systems"
    - "50 years of test un fairness lessons for machine learning"
    - "toward fairness in ai for people with disabilities a research roadmap"
    - "discrimination in the age of algorithms"
    - "learning adversarially fair and transferable representations"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-8 (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"
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "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"

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)