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: "building a regular decision boundary with deep networks"
  Abstract: In this work, we build a generic architecture of Convolutional Neural Networks to discover empirical properties of neural networks. Our first contribution is to introduce a state-of-the-art framework that depends upon few hyper parameters and to study the network when we vary them. It has no max pooling, no biases, only 13 layers, is purely convolutional and yields up to 95.4% and 79.6% accuracy respectively on CIFAR10 and CIFAR100. We show that the nonlinearity of a deep network does not need to be continuous, non expansive or point-wise, to achieve good performance. We show that increasing the width of our network permits being competitive with very deep networks. Our second contribution is an analysis of the contraction and separation properties of this network. Indeed, a 1-nearest neighbor classifier applied on deep features progressively improves with depth, which indicates that the representation is progressively more regular. Besides, we defined and analyzed local support vectors that separate classes locally. All our experiments are reproducible and code is available online, based on TensorFlow.

Connected through hyperedge HE-1 (induced by source paper "scattering networks for hybrid representation learning"):
  with members:
    - "generative networks as inverse problems with scattering transforms"
    - "deep unsupervised learning through spatial contrasting"
    - "scaling the scattering transform deep hybrid networks"
    - "look listen and learn"
    - "improved training of wasserstein gans"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "greedy layerwise learning can scale to imagenet"):
  with members:
    - "scattering networks for hybrid representation learning"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "a provably correct algorithm for deep learning that actually works"
    - "i revnet deep invertible networks"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-3 (induced by source paper "i revnet deep invertible networks"):
  with members:
    - "the reversible residual network backpropagation without storing activations"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "density estimation using real nvp"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "wide residual networks"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "robustness via deep low rank representations"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "speeding up convolutional neural networks with low rank expansions"

Connected through hyperedge HE-5 (induced by source paper "scaling the scattering transform deep hybrid networks"):
  with members:
    - "deep unsupervised learning through spatial contrasting"
    - "multiscale hierarchical convolutional networks"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "wide residual networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

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)