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: "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
  Abstract: This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either removing redundant computations or using fully convolutional architectures that inherently predict many pixels at once. #R##N#The implementations of the three models are accessible through a new utility on top of the Caffe library. The utility provides support for a wide range of image input and output formats, pre-processing parameters and methods to equalize the label histogram during training. The Caffe library has been extended by new layers and a new backend for availability on a wider range of hardware such as CPUs and GPUs through OpenCL. #R##N#On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and $320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding window) network as the baseline. The label throughput is up to one megapixel per second. #R##N#The analyzed neural networks have distinctive characteristics that apply during training or processing, and not every data set is suitable to every architecture. The quality of the predictions is assessed on two neural tissue data sets, of which one is the ISBI 2012 challenge data set. Two different loss functions, Malis loss and Softmax loss, were used during training. #R##N#The whole pipeline, consisting of models, interface and modified Caffe library, is available as Open Source software under the working title Project Greentea.

Connected through hyperedge HE-1 (induced by source paper "fecaffe fpga enabled caffe with opencl for deep learning training and inference on intel stratix 10"):
  with members:
    - "a scalable framework for acceleration of cnn training on deeply pipelined fpga clusters with weight and workload balancing"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "caffeinated fpgas fpga framework for convolutional neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "clblast a tuned opencl blas library"):
  with members:
    - "input aware auto tuning of compute bound hpc kernels"
    - "sobol tensor trains for global sensitivity analysis"
    - "mixed precision training"
    - "parallel multi channel convolution using general matrix multiplication"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "cltune a generic auto tuner for opencl kernels"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-3 (induced by source paper "rapid exact signal scanning with deep convolutional neural networks"):
  with members:
    - "znni maximizing the inference throughput of 3d convolutional networks on multi core cpus and gpus"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "multi scale context aggregation by dilated convolutions"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "densenet implementing efficient convnet descriptor pyramids"
    - "image super resolution using deep convolutional networks"
    - "highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification"

Connected through hyperedge HE-4 (induced by source paper "deep tensor convolution on multicores"):
  with members:
    - "photo realistic single image super resolution using a generative adversarial network"
    - "benchmarking state of the art deep learning software tools"
    - "znni maximizing the inference throughput of 3d convolutional networks on multi core cpus and gpus"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"):
  with members:
    - "gemmbench a framework for reproducible and collaborative benchmarking of matrix multiplication"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a performance comparison of sort and scan libraries for gpus"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "cltune a generic auto tuner for opencl kernels"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "a metaprogramming and autotuning framework for deploying deep learning applications"):
  with members:
    - "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"
    - "boda rtc productive generation of portable efficient code for convolutional neural networks on mobile computing platforms"
    - "multi scale context aggregation by dilated convolutions"
    - "maxdnn an efficient convolution kernel for deep learning with maxwell gpus"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"
    - "deformable part models are convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "znni maximizing the inference throughput of 3d convolutional networks on multi core cpus and gpus"):
  with members:
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "multi scale context aggregation by dilated convolutions"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "fully convolutional networks for semantic segmentation"
    - "fast training of convolutional networks through ffts"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "caffeinated fpgas fpga framework for convolutional neural networks"):
  with members:
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"
    - "overfeat integrated recognition localization and detection using 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.CV(Computer Vision and Pattern Recognition)