human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "specnet spectral domain convolutional neural network"):
  Abstract: The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with increasing depth of the network, which is a major constraint for efficient network training and inference on modern GPUs with limited memory. Several studies show that the feature maps (as generated after the convolutional layers) are the main bottleneck in this memory problem. Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain. Although embedding CNN architectures in the spectral domain is widely exploited to accelerate the training process, we demonstrate that it is also possible to use the spectral domain to reduce the memory footprint by proposing a Spectral Domain Convolutional Neural Network (SpecNet) that performs both the convolution and the activation operations in the spectral domain. SpecNet exploits a configurable threshold to force small values in the feature maps to zero, allowing the feature maps to be stored sparsely. SpecNet also employs a special activation function that preserves the sparsity of the feature maps while effectively encouraging the convergence of the network. The performance of SpecNet is evaluated on three competitive object recognition benchmark tasks (MNIST, CIFAR-10, and SVHN), and compared with four state-of-the-art implementations (LeNet, AlexNet, VGG, and DenseNet). Overall, SpecNet is able to reduce memory consumption by about 60% without significant loss of performance for all tested network architectures.

Includes members:
  Member "efficient memory management for gpu based deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "exploiting kernel sparsity and entropy for interpretable cnn compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression")
  Member "sort second order response transform for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking normalization and elimination singularity in neural networks")
  Member "memory efficient implementation of densenets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "wavelet convolutional neural networks for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "harmonic convolutional networks based on discrete cosine transform")
  Member "the power of sparsity in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is the state of neural network pruning")
  Member "a survey of model compression and acceleration for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "in place activated batchnorm for memory optimized training of dnns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")

Question: Please tell me which class the source paper 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)