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: "a taxonomy and library for visualizing learned features in convolutional neural networks"
  Abstract: Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging task. To remedy this unsatisfactory situation, a number of groups have recently proposed different methods to visualize the learned models. In this work we suggest a general taxonomy to classify and compare these methods, subdividing the literature into three main categories and providing researchers with a terminology to base their works on. Furthermore, we introduce the FeatureVis library for MatConvNet: an extendable, easy to use open source library for visualizing CNNs. It contains implementations from each of the three main classes of visualization methods and serves as a useful tool for an enhanced understanding of the features learned by intermediate layers, as well as for the analysis of why a network might fail for certain examples.

Connected through hyperedge HE-1 (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"
    - "evaluating feature importance estimates"

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"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "improving transparency of deep neural inference process"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "open problems in engineering and quality assurance of safety critical machine learning systems"
    - "visual analytics for explainable deep learning"
    - "explainable software analytics"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

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"
    - "visualbackprop efficient visualization of cnns"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "explanation methods in deep learning users values concerns and challenges"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "audio adversarial examples targeted attacks on speech to text"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interpretable explorable approximations of black box models"
    - "detecting adversarial attacks on neural network policies with visual foresight"
    - "recurrent relational networks"

Connected through hyperedge HE-6 (induced by source paper "do convolutional neural networks learn class hierarchy"):
  with members:
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "direct manipulation visualization of deep networks"
    - "towards better analysis of deep convolutional neural networks"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "decision forests convolutional networks and the models in between"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "stochastic attraction repulsion embedding for large scale image localization"):
  with members:
    - "cplanet enhancing image geolocalization by combinatorial partitioning of maps"
    - "sampling matters in deep embedding learning"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "deep metric learning with angular loss"
    - "revisiting im2gps in the deep learning era"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-8 (induced by source paper "understanding regularization to visualize convolutional neural networks"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "axiomatic attribution for deep networks"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"

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)