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: "enabling sparse winograd convolution by native pruning"
  Abstract: Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs. Sparse Winograd merges these two and thus has the potential to offer a combined performance benefit. Nevertheless, training convolution layers so that the resulting Winograd kernels are sparse has not hitherto been very successful. By introducing a Winograd layer in place of a standard convolution layer, we can learn and prune Winograd coefficients "natively" and obtain sparsity level beyond 90% with only 0.1% accuracy loss with AlexNet on ImageNet dataset. Furthermore, we present a sparse Winograd convolution algorithm and implementation that exploits the sparsity, achieving up to 31.7 effective TFLOP/s in 32-bit precision on a latest Intel Xeon CPU, which corresponds to a 5.4x speedup over a state-of-the-art dense convolution implementation.

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"
    - "efficient sparse winograd convolutional neural networks"
    - "densely connected convolutional networks"
    - "deep tensor convolution on multicores"

Connected through hyperedge HE-2 (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"
    - "efficient sparse winograd convolutional neural networks"
    - "fft convolutions are faster than winograd on modern cpus here is why"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-3 (induced by source paper "jointly sparse convolutional neural networks in dual spatial winograd domains"):
  with members:
    - "universal deep neural network compression"
    - "compressing neural networks using the variational information bottleneck"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards the limit of network quantization"
    - "learning structured sparsity in deep neural networks"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "dynamic network surgery for efficient dnns"

Connected through hyperedge HE-4 (induced by source paper "compressing complex convolutional neural network based on an improved deep compression algorithm"):
  with members:
    - "efficient sparse winograd convolutional neural networks"
    - "deep quaternion networks"
    - "deep complex networks"
    - "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-5 (induced by source paper "efficient winograd convolution via integer arithmetic"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "imagenet large scale visual recognition challenge"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "synaptic strength for convolutional neural network"):
  with members:
    - "efficient sparse winograd convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "interleaved group convolutions for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-7 (induced by source paper "escoin efficient sparse convolutional neural network inference on gpus"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "gardenia a domain specific benchmark suite for next generation accelerators"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "learning structured sparsity in deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "compression of deep convolutional neural networks under joint sparsity constraints"):
  with members:
    - "universal deep neural network compression"
    - "compressing neural networks using the variational information bottleneck"
    - "ct srcnn cascade trained and trimmed deep convolutional neural networks for image super resolution"
    - "towards the limit of network quantization"
    - "learning structured sparsity in deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "fast convnets using group wise brain damage"

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)