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 vision learning to see through algorithmic abstraction"
  Abstract: Learning to see through data is central to contemporary forms of algorithmic knowledge production. While often represented as a mechanical application of rules, making algorithms work with data requires a great deal of situated work. This paper examines how the often-divergent demands of mechanization and discretion manifest in data analytic learning environments. Drawing on research in CSCW and the social sciences, and ethnographic fieldwork in two data learning environments, we show how an algorithm's application is seen sometimes as a mechanical sequence of rules and at other times as an array of situated decisions. Casting data analytics as a rule-based (rather than rule-bound) practice, we show that effective data vision requires would-be analysts to straddle the competing demands of formal abstraction and empirical contingency. We conclude by discussing how the notion of data vision can help better leverage the role of human work in data analytic learning, research, and practice.

Connected through hyperedge HE-1 (induced by source paper "how do data science workers collaborate roles workflows and tools"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "an admm based framework for automl pipeline configuration"
    - "survey on automated machine learning"
    - "datasheets for datasets"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "datahub collaborative data science dataset version management at scale"

Connected through hyperedge HE-2 (induced by source paper "business mis use cases of generative ai"):
  with members:
    - "an exploratory study on visual exploration of model simulations by multiple types of experts"
    - "limits of deepfake detection a robust estimation viewpoint"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "this thing called fairness disciplinary confusion realizing a value in technology"):
  with members:
    - "the disparate effects of strategic manipulation"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "the social cost of strategic classification"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-4 (induced by source paper "human ai collaboration in data science exploring data scientists perceptions of automated ai"):
  with members:
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "meta learning a survey"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "the many faces of data centric workflow optimization a survey"
    - "one button machine for automating feature engineering in relational databases"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "an exploratory study on visual exploration of model simulations by multiple types of experts"):
  with members:
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "effects of sensemaking translucence on distributed collaborative analysis"

Connected through hyperedge HE-6 (induced by source paper "problem formulation and fairness"):
  with members:
    - "trust in data science collaboration translation and accountability in corporate data science projects"

Connected through hyperedge HE-7 (induced by source paper "trust in data science collaboration translation and accountability in corporate data science projects"):
  with members:
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "logics and practices of transparency and opacity in real world applications of public sector machine learning"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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.HC(Human-Computer Interaction)