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: "an intersectional definition of fairness"
  Abstract: We introduce a measure of fairness for algorithms and data with regard to multiple protected attributes. Our proposed definition, differential fairness, is informed by the framework of intersectionality, which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we illustrate links to differential privacy. A case study on census data demonstrates the utility of our approach.

Connected through hyperedge HE-1 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "measurement and fairness"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "theory in theory out the uses of social theory in machine learning for social science"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-3 (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-4 (induced by source paper "50 years of test un fairness lessons for machine learning"):
  with members:
    - "mitigating unwanted biases with adversarial learning"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"
    - "inclusivefacenet improving face attribute detection with race and gender diversity"
    - "runaway feedback loops in predictive policing"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-5 (induced by source paper "awareness in practice tensions in access to sensitive attribute data for antidiscrimination"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "problem formulation and fairness"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "the cost of fairness in classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-6 (induced by source paper "multi differential fairness auditor for black box classifiers"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "differentially private fair learning"
    - "the frontiers of fairness in machine learning"
    - "fairness through computationally bounded awareness"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "a reductions approach to fair classification"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibration for the computationally identifiable masses"

Connected through hyperedge HE-7 (induced by source paper "a mulching proposal"):
  with members:
    - "data science as political action grounding data science in a politics of justice"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "auditing search engines for differential satisfaction across demographics"

Connected through hyperedge HE-8 (induced by source paper "bayesian modeling of intersectional fairness the variance of bias"):
  with members:
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"
    - "a convex framework for fair regression"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "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.LG(Machine Learning)