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 "preference informed fairness"):
  Abstract: As algorithms are increasingly used to make important decisions pertaining to individuals, algorithmic discrimination is becoming a prominent concern. The seminal work of Dwork et al. [ITCS 2012] introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes. In this work, we study fairness when individuals have diverse preferences over the possible outcomes. We show that in such settings, individual fairness can be too restrictive: requiring individual fairness can lead to less-preferred outcomes for the very individuals that IF aims to protect (e.g. a protected minority group). #R##N#We introduce and study a new notion of preference-informed individual fairness (PIIF), a relaxation of individual fairness that allows for outcomes that deviate from IF, provided the deviations are in line with individuals' preferences. We show that PIIF can allow for solutions that are considerably more beneficial to individuals than the best IF solution. We further show how to efficiently optimize any convex objective over the outcomes subject to PIIF, for a rich class of individual preferences. #R##N#Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task setting introduced by Dwork and Ilvento [ITCS 2019]. We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting.

Includes members:
  Member "discrimination through optimization how facebook s ad delivery can lead to biased outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pots protective optimization technologies")
  Member "individual fairness in sponsored search auctions":
    also connected through other hyperedges:
      (none)
  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 "envy free classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a learning framework for distribution based game theoretic solution concepts")
  Member "online learning with an unknown fairness metric":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "fairness through computationally bounded awareness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "personalization of deep learning")
  Member "preventing fairness gerrymandering auditing and learning for subgroup fairness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "to split or not to split the impact of disparate treatment in classification")
  Member "calibration for the computationally identifiable masses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "putting fairness principles into practice challenges metrics and improvements")

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.LG(Machine Learning)