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 disparate effects of strategic manipulation"
  Abstract: When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval. Models of agent responsiveness, termed "strategic manipulation," analyze the interaction between a learner and agents in a world where all agents are equally able to manipulate their features in an attempt to "trick" a published classifier. In cases of real world classification, however, an agent's ability to adapt to an algorithm is not simply a function of her personal interest in receiving a positive classification, but is bound up in a complex web of social factors that affect her ability to pursue certain action responses. In this paper, we adapt models of strategic manipulation to capture dynamics that may arise in a setting of social inequality wherein candidate groups face different costs to manipulation. We find that whenever one group's costs are higher than the other's, the learner's equilibrium strategy exhibits an inequality-reinforcing phenomenon wherein the learner erroneously admits some members of the advantaged group, while erroneously excluding some members of the disadvantaged group. We also consider the effects of interventions in which a learner subsidizes members of the disadvantaged group, lowering their costs in order to improve her own classification performance. Here we encounter a paradoxical result: there exist cases in which providing a subsidy improves only the learner's utility while actually making both candidate groups worse-off---even the group receiving the subsidy. Our results reveal the potentially adverse social ramifications of deploying tools that attempt to evaluate an individual's "quality" when agents' capacities to adaptively respond differ.

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"
    - "optimal decision making under strategic behavior"
    - "fair decisions despite imperfect predictions"
    - "strategic adaptation to classifiers a causal perspective"
    - "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 "causal feature discovery through strategic modification"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "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-3 (induced by source paper "performative prediction"):
  with members:
    - "optimal decision making under strategic behavior"
    - "strategic adaptation to classifiers a causal perspective"
    - "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-4 (induced by source paper "on consequentialism and fairness"):
  with members:
    - "welfare and distributional impacts of fair classification"
    - "fairness in machine learning lessons from political philosophy"
    - "equality of opportunity in supervised learning"

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"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "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"
    - "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-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"
    - "downstream effects of affirmative action"
    - "a short term intervention for long term fairness in the labor market"
    - "the social cost of strategic classification"
    - "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:
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "the social cost of strategic classification"
    - "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)