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: "data science as political action grounding data science in a politics of justice"
  Abstract: In response to recent controversies, the field of data science has rushed to adopt codes of ethics. Such professional codes, however, are ill-equipped to address broad matters of social justice. Instead of ethics codes, I argue, the field must embrace politics. Data scientists must recognize themselves as political actors engaged in normative constructions of society and, as befits political work, evaluate their work according to its downstream material impacts on people's lives. #R##N#I justify this notion in two parts: first, by articulating why data scientists must recognize themselves as political actors, and second, by describing how the field can evolve toward a deliberative and rigorous grounding in a politics of social justice. #R##N#Part 1 responds to three common arguments that have been invoked by data scientists when they are challenged to take political positions regarding their work. In confronting these arguments, I will demonstrate why attempting to remain apolitical is itself a political stance--a fundamentally conservative one--and why the field's current attempts to promote "social good" dangerously rely on vague and unarticulated political assumptions. #R##N#Part 2 proposes a framework for what a politically-engaged data science could look like and how to achieve it, recognizing the challenge of reforming the field in this manner. I conceptualize the process of incorporating politics into data science in four stages: becoming interested in directly addressing social issues, recognizing the politics underlying these issues, redirecting existing methods toward new applications, and, finally, developing new practices and methods that orient data science around a mission of social justice. The path ahead does not require data scientists to abandon their technical expertise, but it does entail expanding their notions of what problems to work on and how to engage with society.

Connected through hyperedge HE-1 (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"
    - "using search queries to understand health information needs in africa"
    - "datasheets for datasets"

Connected through hyperedge HE-2 (induced by source paper "fair and unbiased algorithmic decision making current state and future challenges"):
  with members:
    - "delayed impact of fair machine learning"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"
    - "detecting and correcting for label shift with black box predictors"
    - "the frontiers of fairness in machine learning"
    - "improving palliative care with deep learning"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (induced by source paper "an algorithmic equity toolkit for technology audits by community advocates and activists"):
  with members:
    - "an experimental study of cryptocurrency market dynamics"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-4 (induced by source paper "a mulching proposal"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "an intersectional definition of fairness"
    - "auditing search engines for differential satisfaction across demographics"

Connected through hyperedge HE-5 (induced by source paper "hard choices in artificial intelligence addressing normative uncertainty through sociotechnical commitments"):
  with members:
    - "incomplete contracting and ai alignment"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"

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)