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 survey on measuring indirect discrimination in machine learning"
  Abstract: Nowadays, many decisions are made using predictive models built on historical data.Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition, statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models.

Connected through hyperedge HE-1 (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"
    - "a nutritional label for rankings"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "detection and mitigation of bias in ted talk ratings"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial robustness toolbox v1 0 0"
    - "on fairness and calibration"
    - "automatic text scoring using neural networks"
    - "distributed representations of sentences and documents"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "fairness aware learning with prejudice free representations"):
  with members:
    - "faht an adaptive fairness aware decision tree classifier"

Connected through hyperedge HE-4 (induced by source paper "auditing and achieving intersectional fairness in classification problems"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "fairvis visual analytics for discovering intersectional bias in machine learning"
    - "multiaccuracy black box post processing for fairness in classification"
    - "automated data slicing for model validation a big data ai integration approach"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "differentially private fair learning"
    - "an empirical study of rich subgroup fairness for machine learning"

Connected through hyperedge HE-5 (induced by source paper "fairness in representation quantifying stereotyping as a representational harm"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "fair algorithms for clustering"
    - "understanding the origins of bias in word embeddings"
    - "convex formulations for fair principal component analysis"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "learning adversarially fair and transferable representations"
    - "fair coresets and streaming algorithms for fair k means clustering"

Connected through hyperedge HE-6 (induced by source paper "learning fair representations via an adversarial framework"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "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"
    - "on fairness and calibration"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-7 (induced by source paper "data management for causal algorithmic fairness"):
  with members:
    - "capuchin causal database repair for algorithmic fairness"
    - "causal reasoning for algorithmic fairness"
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "snorkel rapid training data creation with weak supervision"
    - "computing optimal repairs for functional dependencies"
    - "algorithmic decision making and the cost of fairness"
    - "quantifying causal effects on query answering in databases"

Connected through hyperedge HE-8 (induced by source paper "fairyted a fair rating predictor for ted talk data"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"
    - "a survey on bias and fairness in machine learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "predictive inequity in object detection"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rawlsian fairness for 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.CY(Computers and Society)