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: "visualbackprop efficient visualization of cnns"
  Abstract: This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. it was designed to require less computations than a forward propagation. This makes the presented visualization method a valuable debugging tool which can be easily used during both training and inference. We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. Our theoretical findings stand in agreement with the experimental results. The empirical evaluation shows the plausibility of the proposed approach on the road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and simultaneously achieves order of magnitude speed-ups.

Connected through hyperedge HE-1 (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-2 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "the elephant in the room"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-3 (induced by source paper "deep relevance regularization interpretable and robust tumor typing of imaging mass spectrometry data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "not just a black box learning important features through propagating activation differences"

Connected through hyperedge HE-4 (induced by source paper "visual analytics in deep learning an interrogative survey for the next frontiers"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "beyond saliency understanding convolutional neural networks from saliency prediction on layer wise relevance propagation"
    - "the challenge of crafting intelligible intelligence"
    - "methods for interpreting and understanding deep neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "visual analytics for explainable deep learning"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "rnnbow visualizing learning via backpropagation gradients in rnns"

Connected through hyperedge HE-5 (induced by source paper "rethinking self driving multi task knowledge for better generalization and accident explanation ability"):
  with members:
    - "smoothgrad removing noise by adding noise"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "carla an open urban driving simulator"
    - "axiomatic attribution for deep networks"
    - "fast scene understanding for autonomous driving"
    - "understanding convolution for semantic segmentation"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-6 (induced by source paper "end to end multi modal multi task vehicle control for self driving cars with visual perception"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "brain inspired cognitive model with attention for self driving cars"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"
    - "an empirical evaluation of deep learning on highway driving"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-7 (induced by source paper "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"):
  with members:
    - "end to end learning for self driving cars"
    - "concrete problems in ai safety"

Connected through hyperedge HE-8 (induced by source paper "see towards semi supervised end to end scene text recognition"):
  with members:
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "end to end interpretation of the french street name signs dataset"
    - "attention based extraction of structured information from street view imagery"
    - "synthetic data for text localisation in natural images"
    - "recurrent spatial transformer networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "instance aware semantic segmentation via multi task network cascades"
    - "spatial transformer 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.CV(Computer Vision and Pattern Recognition)