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: "the dataset nutrition label a framework to drive higher data quality standards"
  Abstract: Artificial intelligence (AI) systems built on incomplete or biased data will often exhibit problematic outcomes. Current methods of data analysis, particularly before model development, are costly and not standardized. The Dataset Nutrition Label (the Label) is a diagnostic framework that lowers the barrier to standardized data analysis by providing a distilled yet comprehensive overview of dataset "ingredients" before AI model development. Building a Label that can be applied across domains and data types requires that the framework itself be flexible and adaptable; as such, the Label is comprised of diverse qualitative and quantitative modules generated through multiple statistical and probabilistic modelling backends, but displayed in a standardized format. To demonstrate and advance this concept, we generated and published an open source prototype with seven sample modules on the ProPublica Dollars for Docs dataset. The benefits of the Label are manyfold. For data specialists, the Label will drive more robust data analysis practices, provide an efficient way to select the best dataset for their purposes, and increase the overall quality of AI models as a result of more robust training datasets and the ability to check for issues at the time of model development. For those building and publishing datasets, the Label creates an expectation of explanation, which will drive better data collection practices. We also explore the limitations of the Label, including the challenges of generalizing across diverse datasets, and the risk of using "ground truth" data as a comparison dataset. We discuss ways to move forward given the limitations identified. Lastly, we lay out future directions for the Dataset Nutrition Label project, including research and public policy agendas to further advance consideration of the concept.

Connected through hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches"):
  with members:
    - "blimey surrogate prediction explanations beyond lime"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "model cards for model reporting"
    - "towards a characterization of explainable systems"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "personalized explanation in machine learning a conceptualization"
    - "face feasible and actionable counterfactual explanations"
    - "the challenge of crafting intelligible intelligence"

Connected through hyperedge HE-2 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

Connected through hyperedge HE-3 (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"
    - "problem formulation and fairness"

Connected through hyperedge HE-4 (induced by source paper "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"):
  with members:
    - "ethically aligned design of autonomous systems industry viewpoint and an empirical study"
    - "model cards for model reporting"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "the global landscape of ai ethics guidelines"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "questioning the assumptions behind fairness solutions"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "manipulating and measuring model interpretability"

Connected through hyperedge HE-5 (induced by source paper "teaching responsible data science charting new pedagogical territory"):
  with members:
    - "problem formulation and fairness"
    - "a nutritional label for rankings"
    - "datasheets for datasets"
    - "explaining explanations an overview of interpretability of machine learning"
    - "manipulating and measuring model interpretability"
    - "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"
    - "problem formulation and fairness"
    - "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"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-7 (induced by source paper "fat forensics a python toolbox for algorithmic fairness accountability and transparency"):
  with members:
    - "a nutritional label for rankings"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "datasheets for datasets"
    - "defining locality for surrogates in post hoc interpretablity"
    - "a reductions approach to fair classification"
    - "a unified approach to interpreting model predictions"
    - "equality of opportunity in supervised learning"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "artificial intelligence strategies for national security and safety standards"):
  with members:
    - "model cards for model reporting"
    - "headline generation learning from decomposed document titles"
    - "a nutritional label for rankings"
    - "blendcac a blockchain enabled decentralized capability based access control for iots"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "datasheets for datasets"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "adversarial examples for evaluating reading comprehension systems"

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.DB(Databases)