human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "decisions counterfactual explanations and strategic behavior"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "fair decisions despite imperfect predictions"
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"

Connected through hyperedge HE-2 (induced by source paper "performative prediction"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "characterizing concept drift"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "causality for machine learning"):
  with members:
    - "recurrent independent mechanisms"
    - "learning robust representations by projecting superficial statistics out"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "causal reasoning from meta reinforcement learning"
    - "competitive training of mixtures of independent deep generative models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "domain generalization via conditional invariant representation"

Connected through hyperedge HE-4 (induced by source paper "the disparate equilibria of algorithmic decision making when individuals invest rationally"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "long term impact of fair machine learning in sequential decision making representation disparity and group retention"
    - "the disparate effects of strategic manipulation"
    - "delayed impact of fair machine learning"
    - "how do classifiers induce agents to invest effort strategically"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"

Connected through hyperedge HE-5 (induced by source paper "strategic classification is causal modeling in disguise"):
  with members:
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "on the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables"

Connected through hyperedge HE-6 (induced by source paper "strategic adaptation to classifiers a causal perspective"):
  with members:
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Question: Please tell me which class the center node 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)