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: "trust in data science collaboration translation and accountability in corporate data science projects"
  Abstract: The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility. Highlighting the collaborative and heterogeneous nature of real-world data science, we show how the management of trust in applied corporate data science settings depends not only on pre-processing and quantification, but also on negotiation and translation. We conclude by discussing the implications of our findings for data science research and practice, both within and beyond CSCW.

Connected through hyperedge HE-1 (induced by source paper "trust in automl exploring information needs for establishing trust in automated machine learning systems"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"
    - "human ai collaboration in data science exploring data scientists perceptions of automated ai"
    - "survey on automated machine learning"

Connected through hyperedge HE-2 (induced by source paper "autoaiviz opening the blackbox of automated artificial intelligence with conditional parallel coordinates"):
  with members:
    - "effect of confidence and explanation on accuracy and trust calibration in ai assisted decision making"
    - "how do data science workers collaborate roles workflows and tools"
    - "questioning the ai informing design practices for explainable ai user experiences"
    - "model cards for model reporting"
    - "how data scientistswork together with domain experts in scientific collaborations to find the right answer or to ask the right question"
    - "an admm based framework for automl pipeline configuration"
    - "conditional parallel coordinates"
    - "atmseer increasing transparency and controllability in automated machine learning"

Connected through hyperedge HE-3 (induced by source paper "problems with shapley value based explanations as feature importance measures"):
  with members:
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "explainable machine learning in deployment"
    - "the explanation game explaining machine learning models with cooperative game theory"
    - "consistent individualized feature attribution for tree ensembles"
    - "manipulating and measuring model interpretability"
    - "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 "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"
    - "data vision learning to see through algorithmic abstraction"
    - "datahub collaborative data science dataset version management at scale"

Connected through hyperedge HE-5 (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"
    - "data vision learning to see through algorithmic abstraction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (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"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "algorithmic decision making and the cost of fairness"
    - "data vision learning to see through algorithmic abstraction"

Connected through hyperedge HE-7 (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"
    - "the many faces of data centric workflow optimization a survey"
    - "data vision learning to see through algorithmic abstraction"
    - "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-8 (induced by source paper "an exploratory study on visual exploration of model simulations by multiple types of experts"):
  with members:
    - "data vision learning to see through algorithmic abstraction"
    - "effects of sensemaking translucence on distributed collaborative analysis"

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)