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: "on vectorization of deep convolutional neural networks for vision tasks"
  Abstract: We recently have witnessed many ground-breaking results in machine learning and computer vision, generated by using deep convolutional neural networks (CNN). While the success mainly stems from the large volume of training data and the deep network architectures, the vector processing hardware (e.g. GPU) undisputedly plays a vital role in modern CNN implementations to support massive computation. Though much attention was paid in the extent literature to understand the algorithmic side of deep CNN, little research was dedicated to the vectorization for scaling up CNNs. In this paper, we studied the vectorization process of key building blocks in deep CNNs, in order to better understand and facilitate parallel implementation. Key steps in training and testing deep CNNs are abstracted as matrix and vector operators, upon which parallelism can be easily achieved. We developed and compared six implementations with various degrees of vectorization with which we illustrated the impact of vectorization on the speed of model training and testing. Besides, a unified CNN framework for both high-level and low-level vision tasks is provided, along with a vectorized Matlab implementation with state-of-the-art speed performance.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural pyramid for image processing"):
  with members:
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "semantic image inpainting with perceptual and contextual losses"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a generic deep architecture for single image reflection removal and image smoothing"):
  with members:
    - "robust optical flow estimation of double layer images under transparency or reflection"
    - "holistically nested edge detection"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "deeply recursive convolutional network for image super resolution"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "motion blur removal via coupled autoencoder"):
  with members:
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "deeply coupled auto encoder networks for cross view classification"

Connected through hyperedge HE-4 (induced by source paper "factorized bilinear models for image recognition"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "look listen and learn a multimodal lstm for speaker identification"):
  with members:
    - "learning deep representation for face alignment with auxiliary attributes"
    - "deep multimodal speaker naming"
    - "deepid3 face recognition with very deep neural networks"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"
    - "facenet a unified embedding for face recognition and clustering"
    - "neural responding machine for short text conversation"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-6 (induced by source paper "optimizing memory efficiency for deep convolutional neural networks on gpus"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-7 (induced by source paper "deep multimodal speaker naming"):
  with members:
    - "speaker following video subtitles"

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)