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: "exploiting local structures with the kronecker layer in convolutional networks"
  Abstract: In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks. We use Kronecker product to exploit the local structures within convolution and fully-connected layers, by replacing the large weight matrices by combinations of multiple Kronecker products of smaller matrices. Just as the Kronecker product is a generalization of the outer product from vectors to matrices, our method is a generalization of the low rank approximation method for convolution neural networks. We also introduce combinations of different shapes of Kronecker product to increase modeling capacity. Experiments on SVHN, scene text recognition and ImageNet dataset demonstrate that we can achieve $3.3 \times$ speedup or $3.6 \times$ parameter reduction with less than 1\% drop in accuracy, showing the effectiveness and efficiency of our method. Moreover, the computation efficiency of Kronecker layer makes using larger feature map possible, which in turn enables us to outperform the previous state-of-the-art on both SVHN(digit recognition) and CASIA-HWDB (handwritten Chinese character recognition) datasets.

Connected through hyperedge HE-1 (induced by source paper "sketch2code generating a website from a paper mockup"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pix2code generating code from a graphical user interface screenshot"
    - "richer convolutional features for edge detection"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"

Connected through hyperedge HE-3 (induced by source paper "tree structured kronecker convolutional network for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cgnet a light weight context guided network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "foveanet perspective aware urban scene parsing"

Connected through hyperedge HE-4 (induced by source paper "a review on deep learning techniques applied to semantic segmentation"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep contrast learning for salient object detection"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-5 (induced by source paper "balanced quantization an effective and efficient approach to quantized neural networks"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "hardware oriented approximation of convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "fpga based low power speech recognition with recurrent neural networks"
    - "bitwise neural networks"
    - "fast compact and high quality lstm rnn based statistical parametric speech synthesizers for mobile devices"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "dynamic network surgery for efficient dnns"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-7 (induced by source paper "kronecker recurrent units"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "low rank passthrough neural networks"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "orthogonal rnns and long memory tasks"
    - "recurrent highway networks"
    - "gradient descent learns linear dynamical systems"

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)