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: "learning explainable models using attribution priors"
  Abstract: Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by constraining the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior. We propose connecting these topics with attribution priors (this https URL), which allow humans to use the common language of attributions to enforce prior expectations about a model's behavior during training. We develop a differentiable axiomatic feature attribution method called expected gradients and show how to directly regularize these attributions during training. We demonstrate the broad applicability of attribution priors ($\Omega$) by presenting three distinct examples that regularize models to behave more intuitively in three different domains: 1) on image data, $\Omega_{\textrm{pixel}}$ encourages models to have piecewise smooth attribution maps; 2) on gene expression data, $\Omega_{\textrm{graph}}$ encourages models to treat functionally related genes similarly; 3) on a health care dataset, $\Omega_{\textrm{sparse}}$ encourages models to rely on fewer features. In all three domains, attribution priors produce models with more intuitive behavior and better generalization performance by encoding constraints that would otherwise be very difficult to encode using standard model priors.

Connected through hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "is attention interpretable"
    - "sanity checks for saliency metrics"

Connected through hyperedge HE-2 (induced by source paper "an adversarial approach for the robust classification of pneumonia from chest radiographs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "transfusion understanding transfer learning for medical imaging"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-3 (induced by source paper "learning deep attribution priors based on prior knowledge"):
  with members:
    - "explaining classifiers with causal concept effect cace"
    - "feature selection using stochastic gates"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-4 (induced by source paper "interpretations are useful penalizing explanations to align neural networks with prior knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "aggregating explainability methods for neural networks stabilizes explanations"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "embedding human knowledge in deep neural network via attention map"
    - "repair removing representation bias by dataset resampling"
    - "incorporating priors with feature attribution on text classification"
    - "attention is not explanation"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "do explanations reflect decisions a machine centric strategy to quantify the performance of explainability algorithms"):
  with members:
    - "human vs machine attention in neural networks a comparative study"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial patch"
    - "allennlp a deep semantic natural language processing platform"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-6 (induced by source paper "explaining models by propagating shapley values of local components"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "generalized integrated gradients a practical method for explaining diverse ensembles"
    - "the many shapley values for model explanation"
    - "explainable ai for trees from local explanations to global understanding"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural 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.LG(Machine Learning)