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 and comparing convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task. To achieve better performance, the complexity of CNNs is continually increasing with deeper and bigger architectures. Though CNNs achieved promising external classification behavior, understanding of their internal work mechanism is still limited. In this work, we attempt to understand the internal work mechanism of CNNs by probing the internal representations in two comprehensive aspects, i.e., visualizing patches in the representation spaces constructed by different layers, and visualizing visual information kept in each layer. We further compare CNNs with different depths and show the advantages brought by deeper architecture.

Connected through hyperedge HE-1 (induced by source paper "srobb targeted perceptual loss for single image super resolution"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "understanding human judgments of causality"):
  with members:
    - "building machines that learn and think like people"
    - "neural style representations and the large scale classification of artistic style"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (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-4 (induced by source paper "explaining nonlinear classification decisions with deep taylor decomposition"):
  with members:
    - "explaining predictions of non linear classifiers in nlp"
    - "show attend and tell neural image caption generation with visual attention"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "analyzing classifiers fisher vectors and deep neural networks"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-5 (induced by source paper "understanding convolutional neural networks with a mathematical model"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "towards better analysis of deep convolutional neural networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "explaining predictions of non linear classifiers in nlp"
    - "a new method to visualize deep neural networks"
    - "understanding neural networks through deep visualization"
    - "evaluating the visualization of what a deep neural network has learned"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "identifying individual facial expressions by deconstructing a neural network"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "explaining predictions of non linear classifiers in nlp"
    - "interpretable deep neural networks for single trial eeg classification"
    - "a new method to visualize deep neural networks"
    - "evaluating the visualization of what a deep neural network has learned"
    - "inverting visual representations with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "how deep is the feature analysis underlying rapid visual categorization"):
  with members:
    - "identity mappings in deep residual networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

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)