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: "understanding intra class knowledge inside cnn"
  Abstract: Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization. In this work, we instead visualize the intra-class knowledge inside CNN to better understand how an object class is represented in the fully-connected layers. #R##N#To invert the intra-class knowledge into more interpretable images, we propose a non-parametric patch prior upon previous CNN visualization models. With it, we show how different "styles" of templates for an object class are organized by CNN in terms of location and content, and represented in a hierarchical and ensemble way. Moreover, such intra-class knowledge can be used in many interesting applications, e.g. style-based image retrieval and style-based object completion.

Connected through hyperedge HE-1 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-2 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "classification regions of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "understanding neural networks via feature visualization a survey"):
  with members:
    - "vectordefense vectorization as a defense to adversarial examples"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "decoding brain representations by multimodal learning of neural activity and visual features"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-4 (induced by source paper "low cost transfer learning of face tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "taskonomy disentangling task transfer learning"
    - "network dissection quantifying interpretability of deep visual representations"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "beyond saliency understanding convolutional neural networks from saliency prediction on layer wise relevance propagation"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "activis visual exploration of industry scale deep neural network models"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "structured attention networks"
    - "attention is all you need"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-6 (induced by source paper "semantics for global and local interpretation of deep neural networks"):
  with members:
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "chip channel wise disentangled interpretation of deep convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "libra r cnn towards balanced learning for object detection"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "mask r cnn"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "ask your neurons a deep learning approach to visual question answering"

Connected through hyperedge HE-8 (induced by source paper "sampling the inverse set of a neuron an approach to understanding neural nets"):
  with members:
    - "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"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "every filter extracts a specific texture in convolutional neural networks"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "understanding deep image representations by inverting them"

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)