human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "embedded ethics integrating ethics broadly across computer science education":
    also connected through other hyperedges:
      (none)
  Member "ai for the common good pitfalls challenges and ethics pen testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tackling climate change with machine learning")

Question: Please tell me which class the source paper 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)