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: "simplicity creates inequity implications for fairness stereotypes and interpretability"
  Abstract: Algorithms are increasingly used to aid, or in some cases supplant, human decision-making, particularly for decisions that hinge on predictions. As a result, two additional features in addition to prediction quality have generated interest: (i) to facilitate human interaction and understanding with these algorithms, we desire prediction functions that are in some fashion simple or interpretable; and (ii) because they influence consequential decisions, we also want them to produce equitable allocations. We develop a formal model to explore the relationship between the demands of simplicity and equity. Although the two concepts appear to be motivated by qualitatively distinct goals, we show a fundamental inconsistency between them. Specifically, we formalize a general framework for producing simple prediction functions, and in this framework we establish two basic results. First, every simple prediction function is strictly improvable: there exists a more complex prediction function that is both strictly more efficient and also strictly more equitable. Put another way, using a simple prediction function both reduces utility for disadvantaged groups and reduces overall welfare relative to other options. Second, we show that simple prediction functions necessarily create incentives to use information about individuals' membership in a disadvantaged group --- incentives that weren't present before simplification, and that work against these individuals. Thus, simplicity transforms disadvantage into bias against the disadvantaged group. Our results are not only about algorithms but about any process that produces simple models, and as such they connect to the psychology of stereotypes and to an earlier economics literature on statistical discrimination.

Connected through hyperedge HE-1 (induced by source paper "a resolution in algorithmic fairness calibrated scores for fair classifications"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

Connected through hyperedge HE-2 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-3 (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"
    - "racial categories in machine learning"
    - "problem formulation and fairness"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "datasheets for datasets"

Connected through hyperedge HE-4 (induced by source paper "fairness sample complexity and the case for human intervention"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "fairness under composition"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "runaway feedback loops in predictive policing"
    - "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)