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: "a nutritional label for rankings"
  Abstract: Algorithmic decisions often result in scoring and ranking individuals to determine credit worthiness, qualifications for college admissions and employment, and compatibility as dating partners. While automatic and seemingly objective, ranking algorithms can discriminate against individuals and protected groups, and exhibit low diversity. Furthermore, ranked results are often unstable -- small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate. Similar concerns apply in cases where items other than individuals are ranked, including colleges, academic departments, or products. Despite the ubiquity of rankers, there is, to the best of our knowledge, no technical work that focuses on making rankers transparent.   In this demonstration we present Ranking Facts, a Web-based application that generates a "nutritional label" for rankings. Ranking Facts is made up of a collection of visual widgets that implement our latest research results on fairness, stability, and transparency for rankings, and that communicate details of the ranking methodology, or of the output, to the end user. We will showcase Ranking Facts on real datasets from different domains, including college rankings, criminal risk assessment, and financial services.

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 "fair active learning"):
  with members:
    - "stable and fair classification"
    - "a survey on bias and fairness in machine learning"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "assessing and remedying coverage for a given dataset"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "active fairness in algorithmic decision making"
    - "bias disparity in recommendation systems"

Connected through hyperedge HE-3 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"
    - "proxy non discrimination in data driven systems"

Connected through hyperedge HE-4 (induced by source paper "teaching responsible data science charting new pedagogical territory"):
  with members:
    - "problem formulation and fairness"
    - "datasheets for datasets"
    - "explaining explanations an overview of interpretability of machine learning"
    - "manipulating and measuring model interpretability"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "a unified approach to interpreting model predictions"
    - "measuring fairness in ranked outputs"
    - "on the im possibility of fairness"

Connected through hyperedge HE-5 (induced by source paper "fat forensics a python toolbox for algorithmic fairness accountability and transparency"):
  with members:
    - "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"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "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-6 (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"
    - "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"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-7 (induced by source paper "computational methods in professional communication"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "intertemporal connections between query suggestions and search engine results for politics related queries"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "biased assimilation homophily and the dynamics of polarization"
    - "four degrees of separation"
    - "indicators of the interdisciplinarity of journals diversity centrality and citations"

Connected through hyperedge HE-8 (induced by source paper "transparency fairness data protection neutrality data management challenges in the face of new regulation"):
  with members:
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "why should i trust you explaining the predictions of any classifier"
    - "fairness through awareness"

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)