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: "assessing the stability of interpretable models"
  Abstract: Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.

Connected through hyperedge HE-1 (induced by source paper "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"):
  with members:
    - "ibreakdown uncertainty of model explanations for non additive predictive models"
    - "model agnostic supervised local explanations"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-2 (induced by source paper "ibreakdown uncertainty of model explanations for non additive predictive models"):
  with members:
    - "consistent individualized feature attribution for tree ensembles"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "on the robustness of interpretability methods"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)