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 promise and peril of human evaluation for model interpretability"
  Abstract: Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this position paper, we propose a distinction between descriptive and persuasive explanations. We discuss reasoning suggesting that functional interpretability may be correlated with cognitive function and user preferences. If this is indeed the case, evaluation and optimization using functional metrics could perpetuate implicit cognitive bias in explanations that threaten transparency. Finally, we propose two potential research directions to disambiguate cognitive function and explanation models, retaining control over the tradeoff between accuracy and interpretability.

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 "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-3 (induced by source paper "evaluating explanation without ground truth in interpretable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an evaluation of the human interpretability of explanation"
    - "learning interpretable models with causal guarantees"
    - "interpretable deep learning in drug discovery"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "neural network attributions a causal perspective"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-4 (induced by source paper "semantically interpretable activation maps what where how explanations within cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradmask reduce overfitting by regularizing saliency"
    - "interpretability beyond classification output semantic bottleneck networks"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "methods for interpreting and understanding deep neural networks"
    - "semantic bottleneck for computer vision tasks"

Connected through hyperedge HE-5 (induced by source paper "learning interpretable models using an oracle"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "optimal resampling for learning small models"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "watch your step learning node embeddings via graph attention"
    - "enriching word vectors with subword information"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "a psychophysics approach for quantitative comparison of interpretable computer vision models"):
  with members:
    - "quantifying interpretability and trust in machine learning systems"
    - "innvestigate neural networks"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-7 (induced by source paper "generating user friendly explanations for loan denials using gans"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "do explanations make vqa models more predictable to a human"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "accountability of ai under the law the role of explanation"
    - "causalgan learning causal implicit generative models with adversarial training"

Connected through hyperedge HE-8 (induced by source paper "interpretability with accurate small models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural predictions with differentiable binary variables"
    - "a unified approach to interpreting model predictions"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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.AI(Artificial Intelligence)