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: "visualizing deep networks by optimizing with integrated gradients"
  Abstract: Understanding and interpreting the decisions made by deep learning models is valuable in many domains. In computer vision, computing heatmaps from a deep network is a popular approach for visualizing and understanding deep networks. However, heatmaps that do not correlate with the network may mislead human, hence the performance of heatmaps in providing a faithful explanation to the underlying deep network is crucial. In this paper, we propose I-GOS, which optimizes for a heatmap so that the classification scores on the masked image would maximally decrease. The main novelty of the approach is to compute descent directions based on the integrated gradients instead of the normal gradient, which avoids local optima and speeds up convergence. Compared with previous approaches, our method can flexibly compute heatmaps at any resolution for different user needs. Extensive experiments on several benchmark datasets show that the heatmaps produced by our approach are more correlated with the decision of the underlying deep network, in comparison with other state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters"):
  with members:
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "towards explainable artificial intelligence"
    - "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 "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep features analysis with attention networks"
    - "how do convolutional neural networks learn design"
    - "innvestigate neural networks"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "understanding individual decisions of cnns via contrastive backpropagation"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-3 (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"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "a benchmark for interpretability methods in deep neural networks"
    - "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-4 (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"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "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"
    - "vital a visual interpretation on text with adversarial learning for image labeling"

Connected through hyperedge HE-5 (induced by source paper "visualizing point cloud classifiers by curvature smoothing"):
  with members:
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"

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)