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: "the social cost of strategic classification"
  Abstract: Consequential decision-making typically incentivizes individuals to behave strategically, tailoring their behavior to the specifics of the decision rule. A long line of work has therefore sought to counteract strategic behavior by designing more conservative decision boundaries in an effort to increase robustness to the effects of strategic covariate shift. #R##N#We show that these efforts benefit the institutional decision maker at the expense of the individuals being classified. Introducing a notion of social burden, we prove that any increase in institutional utility necessarily leads to a corresponding increase in social burden. Moreover, we show that the negative externalities of strategic classification can disproportionately harm disadvantaged groups in the population. #R##N#Our results highlight that strategy-robustness must be weighed against considerations of social welfare and fairness.

Connected through hyperedge HE-1 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-2 (induced by source paper "decisions counterfactual explanations and strategic behavior"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "optimal decision making under strategic behavior"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "causal feature discovery through strategic modification"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "strategic classification from revealed preferences"
    - "truthful linear regression"
    - "budget feasible mechanisms for experimental design"
    - "buying private data without verification"
    - "linear regression as a non cooperative game"

Connected through hyperedge HE-4 (induced by source paper "performative prediction"):
  with members:
    - "optimal decision making under strategic behavior"
    - "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"
    - "characterizing concept drift"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "extracting incentives from black box decisions"):
  with members:
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "synthesizing action sequences for modifying model decisions"
    - "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"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-6 (induced by source paper "the disparate equilibria of algorithmic decision making when individuals invest rationally"):
  with members:
    - "optimal decision making under strategic behavior"
    - "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 measure and mismeasure of fairness a critical review of fair machine learning"

Connected through hyperedge HE-7 (induced by source paper "causal modeling for fairness in dynamical systems"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "the disparate effects of strategic manipulation"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "from fair decision making to social equality"

Connected through hyperedge HE-8 (induced by source paper "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"):
  with members:
    - "the disparate effects of strategic manipulation"
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "strategic classification from revealed preferences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "algorithmic decision making and the cost of fairness"

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)