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: "putting fairness principles into practice challenges metrics and improvements"
  Abstract: As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of machine learning. This research has greatly expanded our understanding of the concerns and challenges in deploying machine learning, but there has been much less work in seeing how the rubber meets the road. #R##N#In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues. We discuss open questions in implementing equality of opportunity and describe our fairness metric, conditional equality, that takes into account distributional differences. Further, we provide a new approach to improve on the fairness metric during model training and demonstrate its efficacy in improving performance for a real-world product

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "scalable fair clustering"

Connected through hyperedge HE-2 (induced by source paper "case study predictive fairness to reduce misdemeanor recidivism through social service interventions"):
  with members:
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on formalizing fairness in prediction with machine learning"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "censoring representations with an adversary"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "fairness in recommendation ranking through pairwise comparisons"):
  with members:
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "estimating position bias without intrusive interventions"
    - "policy learning for fairness in ranking"
    - "equity of attention amortizing individual fairness in rankings"
    - "selection problems in the presence of implicit bias"
    - "mitigating unwanted biases with adversarial learning"
    - "top k off policy correction for a reinforce recommender system"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-5 (induced by source paper "practical compositional fairness understanding fairness in multi task ml systems"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "pairwise fairness for ranking and regression"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "multiaccuracy black box post processing for fairness in classification"
    - "fairness under composition"

Connected through hyperedge HE-6 (induced by source paper "toward a better trade off between performance and fairness with kernel based distribution matching"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-7 (induced by source paper "transfer of machine learning fairness across domains"):
  with members:
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "proxy fairness"
    - "towards robust and privacy preserving text representations"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "equality of opportunity in supervised learning"
    - "domain separation networks"

Connected through hyperedge HE-8 (induced by source paper "fairvis visual analytics for discovering intersectional bias in machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "predictive inequity in object detection"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "fairgan fairness aware generative adversarial networks"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "equality of opportunity in supervised 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)