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: "interpretability via model extraction"
  Abstract: The ability to interpret machine learning models has become increasingly important now that machine learning is used to inform consequential decisions. We propose an approach called model extraction for interpreting complex, blackbox models. Our approach approximates the complex model using a much more interpretable model; as long as the approximation quality is good, then statistical properties of the complex model are reflected in the interpretable model. We show how model extraction can be used to understand and debug random forests and neural nets trained on several datasets from the UCI Machine Learning Repository, as well as control policies learned for several classical reinforcement learning problems.

Connected through hyperedge HE-1 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "personalized explanation in machine learning"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "explaining models an empirical study of how explanations impact fairness judgment"
    - "the challenge of crafting intelligible intelligence"
    - "metrics for explainable ai challenges and prospects"

Connected through hyperedge HE-2 (induced by source paper "interpretability of blackbox machine learning models through dataview extraction and shadow model creation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a framework for understanding unintended consequences of machine learning"
    - "explaining deep learning models a bayesian non parametric approach"
    - "explaining explanations an overview of interpretability of machine learning"
    - "magix model agnostic globally interpretable explanations"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "on interpretability of artificial neural networks"):
  with members:
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "sanity checks for saliency maps"
    - "explaining recurrent neural network predictions in sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local rule based explanations of black box decision systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "experimental resilience assessment of an open source driving agent"
    - "interpreting deep classifier by visual distillation of dark knowledge"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "software engineering challenges of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a framework for the extraction of deep neural networks by leveraging public data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "generative adversarial networks for black box api attacks with limited training data"
    - "adversarial active learning for deep networks a margin based approach"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "exploring connections between active learning and model extraction"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-6 (induced by source paper "human grounded evaluations of explanation methods for text classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "looking deeper into deep learning model attribution based explanations of textcnn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "understanding convolutional neural networks for text classification"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "innvestigate neural networks"
    - "towards explainable nlp a generative explanation framework for text classification"

Connected through hyperedge HE-7 (induced by source paper "explaining explanations in ai"):
  with members:
    - "high dimensional spaces deep learning and adversarial examples"
    - "methods for interpreting and understanding deep neural networks"
    - "interpretable explorable approximations of black box models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-8 (induced by source paper "how do i fool you manipulating user trust via misleading black box explanations"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "learning interpretable models with causal guarantees"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "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"

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)