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: "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
  Abstract: Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable in the specific context of verification. Suppose we have a machine learning system that predicts X, and we provide rationale for this prediction X. Given an input, an explanation, and an output, is the output consistent with the input and the supposed rationale? Via a series of user-studies, we identify what kinds of increases in complexity have the greatest effect on the time it takes for humans to verify the rationale, and which seem relatively insensitive.

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 "a study on multimodal and interactive explanations for visual question answering"):
  with members:
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"

Connected through hyperedge HE-3 (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"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-5 (induced by source paper "evaluating saliency map explanations for convolutional neural networks a user study"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "minimalistic explanations capturing the essence of decisions"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "manipulating and measuring model interpretability"
    - "explanation in artificial intelligence insights from the social sciences"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating visual explanations"

Connected through hyperedge HE-6 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-7 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "attacking optical flow"
    - "a general framework for adversarial examples with objectives"
    - "on the in fidelity and sensitivity for explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"

Connected through hyperedge HE-8 (induced by source paper "what clinicians want contextualizing explainable machine learning for clinical end use"):
  with members:
    - "model cards for model reporting"
    - "attention is not explanation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "opportunities in machine learning for healthcare"
    - "clinicalvis supporting clinical task focused design evaluation"
    - "manipulating and measuring model interpretability"

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)