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: "what can ai do for me evaluating machine learning interpretations in cooperative play"
  Abstract: Machine learning is an important tool for decision making, but its ethical and responsible application requires rigorous vetting of its interpretability and utility: an understudied problem, particularly for natural language processing models. We propose an evaluation of interpretation on a real task with real human users, where the effectiveness of interpretation is measured by how much it improves human performance. We design a grounded, realistic human-computer cooperative setting using a question answering task, Quizbowl. We recruit both trivia experts and novices to play this game with computer as their teammate, who communicates its prediction via three different interpretations. We also provide design guidance for natural language processing human-in-the-loop settings.

Connected through hyperedge HE-1 (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"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "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-2 (induced by source paper "leveraging rationales to improve human task performance"):
  with members:
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "interpreting cnn knowledge via an explanatory graph"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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-5 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "attention is not explanation"
    - "generating token level explanations for natural language inference"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "transformation networks for target oriented sentiment classification"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "training augmentation with adversarial examples for robust speech recognition"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "normlime a new feature importance metric for explaining deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "revisiting the importance of individual units in cnns via ablation"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-7 (induced by source paper "on human predictions with explanations and predictions of machine learning models a case study on deception detection"):
  with members:
    - "do explanations make vqa models more predictable to a human"
    - "teaching categories to human learners with visual explanations"
    - "a unified approach to interpreting model predictions"
    - "algorithmic decision making and the cost of fairness"
    - "improving human machine cooperative visual search with soft highlighting"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "rationalizing neural predictions"

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)