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 new method to visualize deep neural networks"
  Abstract: We present a method for visualising the response of a deep neural network to a specific input. For image data for instance our method will highlight areas that provide evidence in favor of, and against choosing a certain class. The method overcomes several shortcomings of previous methods and provides great additional insight into the decision making process of convolutional networks, which is important both to improve models and to accelerate the adoption of such methods in e.g. medicine. In experiments on ImageNet data, we illustrate how the method works and can be applied in different ways to understand deep neural nets.

Connected through hyperedge HE-1 (induced by source paper "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"):
  with members:
    - "subset scanning over neural network activations"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-2 (induced by source paper "a step towards exposing bias in trained deep convolutional neural network models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "subset scanning over neural network activations"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-3 (induced by source paper "detecting adversarial perturbations with saliency"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "on the statistical detection of adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "early methods for detecting adversarial images"
    - "measuring neural net robustness with constraints"
    - "adversarial examples detection in deep networks with convolutional filter statistics"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "explaining the unique nature of individual gait patterns with deep learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "top down neural attention by excitation backprop"

Connected through hyperedge HE-5 (induced by source paper "conceptual content in deep convolutional neural networks an analysis into multi faceted properties of neurons"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "understanding trained cnns by indexing neuron selectivity"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "understanding neural networks through deep visualization"
    - "deep learning and the information bottleneck principle"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-6 (induced by source paper "mdnet a semantically and visually interpretable medical image diagnosis network"):
  with members:
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "image captioning with semantic attention"
    - "disturblabel regularizing cnn on the loss layer"
    - "deep networks with stochastic depth"
    - "attention correctness in neural image captioning"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"

Connected through hyperedge HE-7 (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"
    - "understanding neural networks through deep visualization"
    - "evaluating the visualization of what a deep neural network has learned"
    - "inverting visual representations with convolutional networks"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-8 (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"
    - "evaluating the visualization of what a deep neural network has learned"
    - "inverting visual representations with convolutional networks"
    - "deep residual learning for image recognition"
    - "analyzing classifiers fisher vectors and deep neural 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)