human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "capuchin causal database repair for algorithmic fairness"):
  Abstract: Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility.

Includes members:
  Member "causal reasoning for algorithmic fairness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "fairness and accountability design needs for algorithmic support in high stakes public sector decision making":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "factors influencing perceived fairness in algorithmic decision making algorithm outcomes development procedures and individual differences")
  Member "bias in olap queries detection explanation and removal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "surfacing visualization mirages")
  Member "fairness testing testing software for discrimination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "computing optimal repairs for functional dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing")
  Member "learning non discriminatory predictors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "algorithmic decision making and the cost of fairness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic fairness")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

Question: Please tell me which class the source paper 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.DB(Databases)