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: "cnncomparator comparative analytics of convolutional neural networks"
  Abstract: Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance. However, training a good CNN model can still be a challenging task. In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance. Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations. In this paper, we present a visual analytics approach to compare two different snapshots of a trained CNN model taken after different numbers of epochs, so as to provide some insight into the design or the training of a better CNN model. Our system compares snapshots by exploring the differences in operation parameters and the corresponding blob data at different levels. A case study has been conducted to demonstrate the effectiveness of our system.

Connected through hyperedge HE-1 (induced by source paper "net2vis a visual grammar for automatically generating publication ready cnn architecture visualizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "ssgan secure steganography based on generative adversarial networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "confusionflow a model agnostic visualization for temporal analysis of classifier confusion"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-3 (induced by source paper "atmseer increasing transparency and controllability in automated machine learning"):
  with members:
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "large scale evolution of image classifiers"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "graying the black box understanding dqns"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural architecture search with reinforcement learning"

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 "deeptracker visualizing the training process of convolutional neural networks"):
  with members:
    - "do convolutional neural networks learn class hierarchy"
    - "activis visual exploration of industry scale deep neural network models"
    - "focal loss for dense object detection"
    - "towards better analysis of deep convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "inverting visual representations with convolutional 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.LG(Machine Learning)