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: "defining locality for surrogates in post hoc interpretablity"
  Abstract: Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local surrogate is trained, in order to approximate accurately the local black-box decision boundary. Unfortunately, as shown in this paper, this issue is not only a parameter or sampling distribution challenge and has a major impact on the relevance and quality of the approximation of the local black-box decision boundary and thus on the meaning and accuracy of the generated explanation. To overcome the identified problems, quantified with an adapted measure and procedure, we propose to generate surrogate-based explanations for individual predictions based on a sampling centered on particular place of the decision boundary, relevant for the prediction to be explained, rather than on the prediction itself as it is classically done. We evaluate the novel approach compared to state-of-the-art methods and a straightforward improvement thereof on four UCI datasets.

Connected through hyperedge HE-1 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches"):
  with members:
    - "blimey surrogate prediction explanations beyond lime"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "model cards for model reporting"
    - "towards a characterization of explainable systems"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "personalized explanation in machine learning a conceptualization"
    - "face feasible and actionable counterfactual explanations"
    - "the challenge of crafting intelligible intelligence"

Connected through hyperedge HE-2 (induced by source paper "can i trust the explainer verifying post hoc explanatory methods"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "evaluating neural network explanation methods using hybrid documents and morphological prediction"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "model agnostic supervised local explanations"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"

Connected through hyperedge HE-3 (induced by source paper "fat forensics a python toolbox for algorithmic fairness accountability and transparency"):
  with members:
    - "a nutritional label for rankings"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "datasheets for datasets"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "a reductions approach to fair classification"
    - "a unified approach to interpreting model predictions"
    - "equality of opportunity in supervised learning"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "issues with post hoc counterfactual explanations a discussion"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "interpretable credit application predictions with counterfactual explanations"
    - "local rule based explanations of black box decision systems"
    - "smoothgrad removing noise by adding noise"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "accountability of ai under the law the role of explanation"
    - "generalized inverse classification"

Connected through hyperedge HE-5 (induced by source paper "the dangers of post hoc interpretability unjustified counterfactual explanations"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "generalized inverse classification"

Connected through hyperedge HE-6 (induced by source paper "blimey surrogate prediction explanations beyond lime"):
  with members:
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "fat forensics a python toolbox for algorithmic fairness accountability and transparency"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "alime autoencoder based approach for local interpretability"):
  with members:
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "example and feature importance based explanations for black box machine learning models"):
  with members:
    - "a survey of methods for explaining black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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)