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: "evaluating feature importance estimates"
  Abstract: Estimating the influence of a given feature to a model prediction is challenging. We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the accuracy of interpretability methods that estimate input feature importance in deep neural networks. We remove a fraction of input features deemed to be most important according to each estimator and measure the change to the model accuracy upon retraining. The most accurate estimator will identify inputs as important whose removal causes the most damage to model performance relative to all other estimators. This evaluation produces thought-provoking results -- we find that several estimators are less accurate than a random assignment of feature importance. However, averaging a set of squared noisy estimators (a variant of a technique proposed by Smilkov et al. (2017)), leads to significant gains in accuracy for each method considered and far outperforms such a random guess.

Connected through hyperedge HE-1 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "counterfactual states for atari agents via generative deep learning"
    - "free lunch saliency via attention in atari agents"
    - "explaining explanations in ai"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "toybox better atari environments for testing reinforcement learning agents"
    - "towards better interpretability in deep q networks"
    - "investigating human priors for playing video games"

Connected through hyperedge HE-2 (induced by source paper "irof a low resource evaluation metric for explanation methods"):
  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"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "saccader improving accuracy of hard attention models for vision"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "attention is not explanation"
    - "do better imagenet models transfer better"
    - "feature denoising for improving adversarial robustness"
    - "regularized evolution for image classifier architecture search"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-5 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial policies attacking deep reinforcement learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "establishing appropriate trust via critical states"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-6 (induced by source paper "free lunch saliency via attention in atari agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "atari head atari human eye tracking and demonstration dataset"
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an introduction to deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "on the in fidelity and sensitivity for explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "towards robust locally linear deep networks"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "counterfactual visual explanations"
    - "interpreting black box predictions using fisher kernels"
    - "representer point selection for explaining deep neural networks"
    - "model agnostic supervised local explanations"

Connected through hyperedge HE-8 (induced by source paper "explainable deep learning for video recognition tasks a framework recommendations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "discriminating spatial and temporal relevance in deep taylor decompositions for explainable activity recognition"
    - "metrics for explainable ai challenges and prospects"
    - "real world anomaly detection in surveillance videos"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "tip typifying the interpretability of procedures"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"

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)