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: "aggregating explainability methods for neural networks stabilizes explanations"
  Abstract: Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation. In fact, most works rely on manually assessing the explanation to evaluate the quality of a method. This injects uncertainty in the explanation process along several dimensions: Which explanation method to apply? Who should we ask to evaluate it and which criteria should be used for the evaluation? Our contributions in this paper are twofold. First, we investigate schemes to combine explanation methods and reduce model uncertainty to obtain a single aggregated explanation. Our findings show that the aggregation is more robust, well-aligned with human explanations and can attribute relevance to a broader set of features (completeness). Second, we propose a novel way of evaluating explanation methods that circumvents the need for manual evaluation and is not reliant on the alignment of neural networks and humans decision processes.

Connected through hyperedge HE-1 (induced by source paper "measuring and improving the quality of visual explanations"):
  with members:
    - "full gradient representation for neural network visualization"
    - "benchmarking attribution methods with relative feature importance"
    - "a benchmark for interpretability methods in deep neural networks"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning explainable models using attribution priors"
    - "attention is not explanation"
    - "interpreting adversarially trained convolutional 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)