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: "how do classifiers induce agents to invest effort strategically"
  Abstract: Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcomes they receive, and we give a tight characterization of when such agents can be incentivized to invest specified forms of effort into improving their outcomes as opposed to "gaming" the classifier. We show that whenever any "reasonable" mechanism can do so, a simple linear mechanism suffices.

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"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "a short term intervention for long term fairness in the labor market"

Connected through hyperedge HE-2 (induced by source paper "the hidden assumptions behind counterfactual explanations and principal reasons"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "delayed impact of fair machine learning"
    - "interpretable credit application predictions with counterfactual explanations"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

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"
    - "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-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"
    - "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-5 (induced by source paper "multiparty dynamics and failure modes for machine learning and artificial intelligence"):
  with members:
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "oversight of unsafe systems via dynamic safety envelopes"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "supervising strong learners by amplifying weak experts"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "robustness to fundamental uncertainty in agi alignment"
    - "data poisoning attacks against online learning"
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-6 (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"
    - "the social cost of strategic classification"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "optimal decision making under strategic behavior"):
  with members:
    - "fair decisions despite imperfect predictions"
    - "the disparate effects of strategic manipulation"
    - "consequential ranking algorithms and long term welfare"
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "from fair decision making to social equality"
    - "deconvolving feedback loops in recommender systems"
    - "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)