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 "an empirical study on learning fairness metrics for compas data with human supervision"):
  Abstract: The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropriate similarity metric. In this work, we attempt to learn such similarity metric from human annotated data. We gather a new dataset of human judgments on a criminal recidivism prediction (COMPAS) task. By assuming the human supervision obeys the principle of individual fairness, we leverage prior work on metric learning, evaluate the performance of several metric learning methods on our dataset, and show that the learned metrics outperform the Euclidean and Precision metric under various criteria. We do not provide a way to directly learn a similarity metric satisfying the individual fairness, but to provide an empirical study on how to derive the similarity metric from human supervisors, then future work can use this as a tool to understand human supervision.

Includes members:
  Member "operationalizing individual fairness with pairwise fair representations":
    also connected through other hyperedges:
      (none)
  Member "metric learning for individual fairness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness")
  Member "eliciting and enforcing subjective individual fairness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness")
  Member "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning")
  Member "ifair learning individually fair data representations for algorithmic decision making":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "calibrated fairness in bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "deep metric learning using triplet network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opengan open set generative adversarial networks")

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.CY(Computers and Society)