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: "interpretable and fine grained visual explanations for convolutional neural networks"
  Abstract: To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.

Connected through hyperedge HE-1 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters"):
  with members:
    - "towards explainable artificial intelligence"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "removing input features via a generative model to explain their attributions to classifier s decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "understanding deep architectures by visual summaries"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-2 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object localization"
    - "robust attribution regularization"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "counterfactual visual explanations"
    - "hardening deep neural networks via adversarial model cascades"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-4 (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-5 (induced by source paper "embedding human knowledge in deep neural network via attention map"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "rise randomized input sampling for explanation of black box models"
    - "smoothgrad removing noise by adding noise"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "removing input features via a generative model to explain their attributions to classifier s decisions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a benchmark for interpretability methods in deep neural networks"
    - "counterfactual visual explanations"
    - "yolov3 an incremental improvement"
    - "methods for interpreting and understanding deep neural networks"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-7 (induced by source paper "improving feature attribution through input specific network pruning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "full gradient representation for neural network visualization"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "a benchmark for interpretability methods in deep neural networks"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "why are saliency maps noisy cause of and solution to noisy saliency maps"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"

Connected through hyperedge HE-8 (induced by source paper "iterative and adaptive sampling with spatial attention for black box model explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "faceforensics learning to detect manipulated facial images"
    - "embedding human knowledge in deep neural network via attention map"
    - "why do these match explaining the behavior of image similarity models"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "argan attentive recurrent generative adversarial network for shadow detection and removal"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "vital a visual interpretation on text with adversarial learning for image labeling"

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.CV(Computer Vision and Pattern Recognition)