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: "racial categories in machine learning"
  Abstract: Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.

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"
    - "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"
    - "an intersectional definition of fairness"

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"
    - "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"
    - "an intersectional definition of fairness"
    - "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 "addressing multiple metrics of group fairness in data driven decision making"):
  with members:
    - "delayed impact of fair 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-4 (induced by source paper "saving face investigating the ethical concerns of facial recognition auditing"):
  with members:
    - "diversity in faces"
    - "model cards for model reporting"
    - "deep learning for face recognition pride or prejudiced"
    - "oxford handbook on ai ethics book chapter on race and gender"
    - "datasheets for datasets"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-5 (induced by source paper "measurement and fairness"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "the scientific method in the science of machine learning"
    - "the profiling potential of computer vision and the challenge of computational empiricism"
    - "problem formulation and fairness"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"

Connected through hyperedge HE-6 (induced by source paper "bias remediation in driver drowsiness detection systems using generative adversarial networks"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "detecting inter sectional accuracy differences in driver drowsiness detection algorithms"
    - "data augmentation for low resource sentiment analysis using generative adversarial networks"
    - "depthwisegans fast training generative adversarial networks for realistic image synthesis"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "text to image synthesis using generative adversarial networks"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "domain adaptive transfer learning with specialist models"

Connected through hyperedge HE-7 (induced by source paper "gaps in information access in social networks"):
  with members:
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "minimizing polarization and disagreement in social networks"
    - "equality of opportunity in supervised learning"
    - "a confidence based approach for balancing fairness and accuracy"
    - "reducing controversy by connecting opposing views"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "fairness through awareness"
    - "pricing strategies for viral marketing on social networks"

Connected through hyperedge HE-8 (induced by source paper "towards a critical race methodology in algorithmic fairness"):
  with members:
    - "model cards for model reporting"
    - "measurement and fairness"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "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.LG(Machine Learning)