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 "interventions for ranking in the presence of implicit bias"):
  Abstract: Implicit bias is the unconscious attribution of particular qualities (or lack thereof) to a member from a particular social group (e.g., defined by gender or race). Studies on implicit bias have shown that these unconscious stereotypes can have adverse outcomes in various social contexts, such as job screening, teaching, or policing. Recently, (Kleinberg and Raghavan, 2018) considered a mathematical model for implicit bias and showed the effectiveness of the Rooney Rule as a constraint to improve the utility of the outcome for certain cases of the subset selection problem. Here we study the problem of designing interventions for the generalization of subset selection -- ranking -- that requires to output an ordered set and is a central primitive in various social and computational contexts. We present a family of simple and interpretable constraints and show that they can optimally mitigate implicit bias for a generalization of the model studied in (Kleinberg and Raghavan, 2018). Subsequently, we prove that under natural distributional assumptions on the utilities of items, simple, Rooney Rule-like, constraints can also surprisingly recover almost all the utility lost due to implicit biases. Finally, we augment our theoretical results with empirical findings on real-world distributions from the IIT-JEE (2009) dataset and the Semantic Scholar Research corpus.

Includes members:
  Member "fairness aware ranking in search recommendation systems with application to linkedin talent search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms")
  Member "coresets for clustering with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair correlation clustering")
  Member "toward controlling discrimination in online ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stable and fair classification")
  Member "pairwise fairness for ranking and regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust optimization for fairness with noisy protected groups")
  Member "fair and diverse dpp based data summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stable and fair classification")
  Member "classification with fairness constraints a meta algorithm with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fairness by explicability and adversarial shap learning")
  Member "fairness of exposure in rankings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fairsight visual analytics for fairness in decision making")
  Member "multiwinner voting with fairness constraints":
    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.CY(Computers and Society)