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: "tip typifying the interpretability of procedures"
  Abstract: We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability. Finally, principled interpretable strategies are proposed and empirically evaluated on synthetic data, as well as on the largest public olfaction dataset that was made recently available \cite{olfs}. We also experiment on MNIST with a simple target model and different oracle models of varying complexity. This leads to the insight that the improvement in the target model is not only a function of the oracle models performance, but also its relative complexity with respect to the target model.

Connected through hyperedge HE-1 (induced by source paper "explainable deep learning for video recognition tasks a framework recommendations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discriminating spatial and temporal relevance in deep taylor decompositions for explainable activity recognition"
    - "metrics for explainable ai challenges and prospects"
    - "real world anomaly detection in surveillance videos"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "evaluating feature importance estimates"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"

Connected through hyperedge HE-2 (induced by source paper "a tour of convolutional networks guided by linear interpreters"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "understanding deep architectures by visual summaries"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-3 (induced by source paper "generating contrastive explanations with monotonic attribute functions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "contrastive explanation a structural model approach"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "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"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-4 (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"
    - "visual explanation by interpretation improving visual feedback capabilities of deep neural networks"
    - "interpreting blackbox models via model extraction"

Connected through hyperedge HE-5 (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"
    - "confidence estimation in deep neural networks via density modelling"
    - "interpreting blackbox models via model extraction"

Connected through hyperedge HE-6 (induced by source paper "interpretable counterfactual explanations guided by prototypes"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "kernel stein tests for multiple model comparison"
    - "on the accuracy of influence functions for measuring group effects"
    - "model agnostic contrastive explanations for structured data"
    - "generating contrastive explanations with monotonic attribute functions"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "towards quantification of explainability in explainable artificial intelligence methods"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "assessing the local interpretability of machine learning models"
    - "manipulating and measuring model interpretability"
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "nonlinear semi parametric models for survival analysis"):
  with members:
    - "machine learning for survival analysis a survey"
    - "adam a method for stochastic optimization"

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)