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: "strategic classification from revealed preferences"
  Abstract: We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome. In rounds, the learner deploys a classifier, and an adversarially chosen agent arrives, possibly manipulating her features to optimally respond to the learner. The learner has no knowledge of the agents' utility functions or "real" features, which may vary widely across agents. Instead, the learner is only able to observe their "revealed preferences" --- i.e. the actual manipulated feature vectors they provide. For a broad family of agent cost functions, we give a computationally efficient learning algorithm that is able to obtain diminishing "Stackelberg regret" --- a form of policy regret that guarantees that the learner is obtaining loss nearly as small as that of the best classifier in hindsight, even allowing for the fact that agents will best-respond differently to the optimal classifier.

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"
    - "how do classifiers induce agents to invest effort strategically"

Connected through hyperedge HE-2 (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"
    - "the social cost of strategic classification"
    - "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 "differentially private call auctions and market impact"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-4 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"

Connected through hyperedge HE-5 (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"
    - "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"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-6 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "low cost learning via active data procurement"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"

Connected through hyperedge HE-7 (induced by source paper "the disparate effects of strategic manipulation"):
  with members:
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "proxy non discrimination in data driven systems"
    - "runaway feedback loops in predictive policing"
    - "causal discrimination discovery through propensity score analysis"
    - "price of transparency in strategic machine learning"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-8 (induced by source paper "nonzero sum adversarial hypothesis testing games"):
  with members:
    - "detection games under fully active adversaries"
    - "sok security and privacy in machine learning"
    - "a game theoretic analysis of adversarial classification"
    - "adversarial hypothesis testing and a quantum stein s lemma for restricted measurements"
    - "binary hypothesis testing game with training data"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

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)