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 explanation game explaining machine learning models with cooperative game theory"
  Abstract: Recently, a number of techniques have been proposed to explain a machine learning (ML) model's prediction by attributing it to the corresponding input features. Popular among these are techniques that apply the Shapley value method from cooperative game theory. While existing papers focus on the axiomatic motivation of Shapley values, and efficient techniques for computing them, they do not justify the game formulations used. For instance, we find that the SHAP algorithm's formulation (Lundberg and Lee 2017) may give substantial attributions to features that play no role in a model. In this work, we study the game formulations underpinning several existing methods. Using a series of simple models, we illustrate how their subtle differences can yield large differences in attribution for the same prediction. We then present a general game formulation that unifies existing methods. After discussing the primitive of single-reference games, we decompose the Shapley values of the general game formulation into Shapley values of single-reference games. This is instructive in several ways. First, it enables confidence intervals on estimated attributions, which are not offered by previous works. Second, it enables different contrastive explanations of a prediction through comparison with different groups of reference inputs. We tie this idea to classic work on Norm Theory (Kahneman and Miller 1986) in cognitive psychology, and propose a general framework for generating explanations for ML models, called formulate, approximate, and explain (FAE). We apply this framework to explaining black-box models trained on two UCI datasets and a Lending Club dataset.

Connected through hyperedge HE-1 (induced by source paper "problems with shapley value based explanations as feature importance measures"):
  with members:
    - "the hidden assumptions behind counterfactual explanations and principal reasons"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "explainable machine learning in deployment"
    - "consistent individualized feature attribution for tree ensembles"
    - "trust in data science collaboration translation and accountability in corporate data science projects"
    - "manipulating and measuring model interpretability"
    - "explanation in artificial intelligence insights from the social sciences"
    - "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)