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 sparse winograd convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd's minimal filtering algorithm (Lavin, 2015) and network pruning (Han et al., 2015) can reduce the operation count, but these two methods cannot be directly combined $-$ applying the Winograd transform fills in the sparsity in both the weights and the activations. We propose two modifications to Winograd-based CNNs to enable these methods to exploit sparsity. First, we move the ReLU operation into the Winograd domain to increase the sparsity of the transformed activations. Second, we prune the weights in the Winograd domain to exploit static weight sparsity. For models on CIFAR-10, CIFAR-100 and ImageNet datasets, our method reduces the number of multiplications by $10.4\times$, $6.8\times$ and $10.8\times$ respectively with loss of accuracy less than $0.1\%$, outperforming previous baselines by $2.0\times$-$3.0\times$. We also show that moving ReLU to the Winograd domain allows more aggressive pruning.

Connected through hyperedge HE-1 (induced by source paper "dwm a decomposable winograd method for convolution acceleration"):
  with members:
    - "winograd convolution for dnns beyond linear polynomials"
    - "efficient winograd convolution via integer arithmetic"
    - "efficient winograd or cook toom convolution kernel implementation on widely used mobile cpus"
    - "compression of deep convolutional neural networks under joint sparsity constraints"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "enabling sparse winograd convolution by native pruning"
    - "densely connected convolutional networks"
    - "deep tensor convolution on multicores"

Connected through hyperedge HE-2 (induced by source paper "accelerated cnn training through gradient approximation"):
  with members:
    - "training simplification and model simplification for deep learning a minimal effort back propagation method"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "nvidia tensor core programmability performance precision"
    - "faster neural network training with approximate tensor operations"
    - "sbnet sparse blocks network for fast inference"
    - "dropback continuous pruning during training"

Connected through hyperedge HE-3 (induced by source paper "rrnet repetition reduction network for energy efficient decoder of depth estimation"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "rtseg real time semantic segmentation comparative study"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"
    - "towards real time unsupervised monocular depth estimation on cpu"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "band limited training and inference for convolutional neural networks"):
  with members:
    - "training behavior of deep neural network in frequency domain"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "training deep neural networks with 8 bit floating point numbers"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "adversarial attacks on neural network policies"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-5 (induced by source paper "single shot channel pruning based on alternating direction method of multipliers"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "progressive weight pruning of deep neural networks using admm"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "large scale gan training for high fidelity natural image synthesis"
    - "snip single shot network pruning based on connection sensitivity"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "sparsetrain leveraging dynamic sparsity in training dnns on general purpose simd processors"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "sparce sparsity aware general purpose core extensions to accelerate deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "compressing complex convolutional neural network based on an improved deep compression algorithm"):
  with members:
    - "deep quaternion networks"
    - "deep complex networks"
    - "enabling sparse winograd convolution by native pruning"
    - "compact deep convolutional neural networks with coarse pruning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-8 (induced by source paper "l3 fusion fast transformed convolutions on cpus"):
  with members:
    - "deep tensor convolution on multicores"
    - "deep residual learning for image recognition"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fast convolutional nets with fbfft a gpu performance evaluation"

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)