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: "interpreting blackbox models via model extraction"
  Abstract: Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose to construct global explanations of complex, blackbox models in the form of a decision tree approximating the original model---as long as the decision tree is a good approximation, then it mirrors the computation performed by the blackbox model. We devise a novel algorithm for extracting decision tree explanations that actively samples new training points to avoid overfitting. We evaluate our algorithm on a random forest to predict diabetes risk and a learned controller for cart-pole. Compared to several baselines, our decision trees are both substantially more accurate and equally or more interpretable based on a user study. Finally, we describe several insights provided by our interpretations, including a causal issue validated by a physician.

Connected through hyperedge HE-1 (induced by source paper "right for the wrong scientific reasons revising deep networks by interacting with their explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "this looks like that deep learning for interpretable image recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-2 (induced by source paper "learning global transparent models from local contrastive explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "model agnostic contrastive explanations for structured data"
    - "improving simple models with confidence profiles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "a unified approach to interpreting model predictions"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "deep weighted averaging classifiers"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "explainable prediction of medical codes from clinical text"
    - "interpreting neural networks with nearest neighbors"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "a unified approach to interpreting model predictions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "copying machine learning classifiers"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "sampling unknown decision functions to build classifier copies"
    - "credit risk prediction in an imbalanced social lending environment"
    - "towards global explanations for credit risk scoring"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "self normalizing neural networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "machine learning models that remember too much"

Connected through hyperedge HE-5 (induced by source paper "cxplain causal explanations for model interpretation under uncertainty"):
  with members:
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "explaining deep learning models a bayesian non parametric approach"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "self normalizing neural networks"

Connected through hyperedge HE-6 (induced by source paper "model agnostic contrastive explanations for structured data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "improving simple models with confidence profiles"
    - "interpreting neural network judgments via minimal stable and symbolic corrections"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "tip typifying the interpretability of procedures"
    - "visual explanation by interpretation improving visual feedback capabilities of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "teaching ai to explain its decisions using embeddings and multi task learning"):
  with members:
    - "ted teaching ai to explain its decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"
    - "boolean decision rules via column generation"
    - "teaching meaningful explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a unified approach to interpreting model predictions"
    - "a formal framework to characterize interpretability of procedures"

Connected through hyperedge HE-8 (induced by source paper "leveraging simple model predictions for enhancing its performance"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "improving simple models with confidence profiles"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "learning to reweight examples for robust deep learning"
    - "on calibration of modern neural networks"
    - "tip typifying the interpretability of procedures"
    - "confidence estimation in deep neural networks via density modelling"

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)