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: "fairness behind a veil of ignorance a welfare analysis for automated decision making"
  Abstract: We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.

Connected through hyperedge HE-1 (induced by source paper "fair by design matching"):
  with members:
    - "balancing lexicographic fairness and a utilitarian objective with application to kidney exchange"
    - "algorithmic decision making and the cost of fairness"
    - "fairness in learning classic and contextual bandits"
    - "nash social welfare approximation for strategic agents"
    - "fairness through awareness"
    - "perfect matchings in o n 1 5 time in regular bipartite graphs"

Connected through hyperedge HE-2 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "fair coresets and streaming algorithms for fair k means clustering"

Connected through hyperedge HE-3 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem"):
  with members:
    - "advancing subgroup fairness via sleeping experts"
    - "fair logistic regression an adversarial perspective"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "combinatorial sleeping bandits with fairness constraints"
    - "policy learning for fairness in ranking"
    - "equal opportunity in online classification with partial feedback"
    - "introduction to multi armed bandits"
    - "online learning with an unknown fairness metric"

Connected through hyperedge HE-4 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-5 (induced by source paper "what is fair exploring pareto efficiency for fairness constraint classifiers"):
  with members:
    - "delayed impact of fair machine learning"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "the price of fair pca one extra dimension"
    - "a reductions approach to fair classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

Connected through hyperedge HE-6 (induced by source paper "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"):
  with members:
    - "the disparate effects of strategic manipulation"
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "the social cost of strategic classification"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "strategic classification from revealed preferences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-7 (induced by source paper "optimized score transformation for fair classification"):
  with members:
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"
    - "controllable invariance through adversarial feature learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "censoring representations with an adversary"

Connected through hyperedge HE-8 (induced by source paper "fair classification and social welfare"):
  with members:
    - "a reductions approach to fair classification"
    - "penalizing unfairness in binary classification"
    - "on fairness and calibration"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "learning non discriminatory predictors"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

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.AI(Artificial Intelligence)