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: "delayed impact of fair machine learning"
  Abstract: Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. #R##N#We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. #R##N#We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. #R##N#Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "balancing competing objectives with noisy data score based classifiers for welfare aware machine learning"):
  with members:
    - "a longitudinal analysis of youtube s promotion of conspiracy videos"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "fairness and utilization in allocating resources with uncertain demand"
    - "fair classification and social welfare"
    - "noise tolerant fair classification"
    - "welfare and distributional impacts of fair classification"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"
    - "multiaccuracy black box post processing for fairness in classification"

Connected through hyperedge HE-3 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond"):
  with members:
    - "average individual fairness algorithms generalization and experiments"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "policy learning for fairness in ranking"
    - "less is better unweighted data subsampling via influence function"
    - "learning optimal fair policies"
    - "causal embeddings for recommendation"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-4 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "adversarial attacks on copyright detection systems"
    - "explaining explanations in ai"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poisoning attacks to graph based recommender systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "questioning the assumptions behind fairness solutions"

Connected through hyperedge HE-5 (induced by source paper "addressing multiple metrics of group fairness in data driven decision making"):
  with members:
    - "racial categories in machine learning"
    - "aequitas a bias and fairness audit toolkit"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "a convex framework for fair regression"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-6 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "interpretable credit application predictions with counterfactual explanations"
    - "how do classifiers induce agents to invest effort strategically"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-7 (induced by source paper "pipeline interventions"):
  with members:
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "fairness under composition"
    - "from fair decision making to social equality"
    - "the frontiers of fairness in machine learning"
    - "gender productivity and prestige in computer science faculty hiring networks"

Connected through hyperedge HE-8 (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"

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.LG(Machine Learning)