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: "an unexpected unity among methods for interpreting model predictions"
  Abstract: Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and provide insight into how the model may be improved. However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability. Recently, several methods have been proposed for interpreting predictions from complex models by estimating the importance of input features. Here, we present how a model-agnostic additive representation of the importance of input features unifies current methods. This representation is optimal, in the sense that it is the only set of additive values that satisfies important properties. We show how we can leverage these properties to create novel visual explanations of model predictions. The thread of unity that this representation weaves through the literature indicates that there are common principles to be learned about the interpretation of model predictions that apply in many scenarios.

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 "optimizing for interpretability in deep neural networks with tree regularization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explanation in artificial intelligence insights from the social sciences"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "distilling a neural network into a soft decision tree"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "harnessing deep neural networks with logic rules"
    - "automatic node selection for deep neural networks using group lasso regularization"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples on graph data deep insights into attack and defense"):
  with members:
    - "simplifying graph convolutional networks"
    - "adversarial attack on graph structured data"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representation learning on graphs with jumping knowledge networks"
    - "attack graph convolutional networks by adding fake nodes"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-4 (induced by source paper "extracting incentives from black box decisions"):
  with members:
    - "towards realistic individual recourse and actionable explanations in black box decision making systems"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "synthesizing action sequences for modifying model decisions"
    - "the disparate effects of strategic manipulation"
    - "efficient search for diverse coherent explanations"
    - "model agnostic counterfactual explanations for consequential decisions"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"

Connected through hyperedge HE-5 (induced by source paper "global aggregations of local explanations for black box models"):
  with members:
    - "explaining explanations in ai"
    - "faithfully explaining rankings in a news recommender system"
    - "lstm a search space odyssey"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "local interpretation methods to machine learning using the domain of the feature space"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "on the robustness of interpretability methods"
    - "end to end learning for self driving cars"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "openml networked science in machine learning"

Connected through hyperedge HE-7 (induced by source paper "technical report a stratification approach to partial dependence for codependent variables"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "training machine learning models by regularizing their explanations"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

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)