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 "optimal decision making under strategic behavior"):
  Abstract: We are witnessing an increasing use of data-driven predictive models to inform decisions. As decisions have implications for individuals and society, there is increasing pressure on decision makers to be transparent about their decision policies, models, and the features they use. At the same time, individuals may use knowledge, gained by transparency, to invest effort strategically in order to maximize their chances of receiving a beneficial decision. In this paper, our goal is to find decision policies that are optimal in terms of utility in such a strategic setting. To this end, we first use the theory of optimal transport to characterize how strategic investment of effort by individuals leads to a change in the feature distribution at a population level. Then, we show that, in contrast with the non-strategic setting, optimal decision policies are stochastic, and we cannot expect to find them in polynomial time. Finally, we derive an efficient greedy algorithm that is guaranteed to find locally optimal decision policies in polynomial time. Experiments on synthetic and real lending data illustrate our theoretical findings and show that the decision policies found by our greedy algorithm achieve higher utility than deterministic threshold rules, which are optimal policies in a non-strategic setting.

Includes members:
  Member "fair decisions despite imperfect predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "the disparate effects of strategic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "consequential ranking algorithms and long term welfare":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair decisions despite imperfect predictions")
  Member "delayed impact of fair machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "how do classifiers induce agents to invest effort strategically":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "a short term intervention for long term fairness in the labor market":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithmic fairness")
  Member "from fair decision making to social equality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "deconvolving feedback loops in recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "consequential ranking algorithms and long term welfare")

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)