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 "accelerating deconvolution on unmodified cnn accelerators for generative adversarial networks a software approach"):
  Abstract: Generative Adversarial Networks (GANs) are the emerging machine learning technology that can learn to automatically create labeled datasets in massive application domains such as speech, image, video and texts. A GAN typically includes a generative model that is taught to generate any distribution of data, and a discriminator trained to distinguish the synthetic data from real-world data. Both convolutional and deconvolutional layers are the major source of performance overhead for GANs and directly impacts the efficiency of GAN-based systems. There are many prior works investigating specialized hardware architectures that can accelerate convolution and deconvolution simultaneously, but they entail intensive hardware modifications to the existing CNN accelerators or processors that focus on convolution acceleration. In contrast, this work proposes a novel deconvolution layer implementation with a software approach and enables fast and efficient generative network inference on the legacy Convolutional Neural Networks (CNNs) accelerators. Our proposed method reorganizes the computation of deconvolutional layer and allows the CNN accelerators to treat it as the standard convolutional layer after we split the original deconvolutional filters into multiple small filters. The proposed data flow is implemented on representative CNN accelerators including dot-production array and regular 2D PE array architectures. Compared to the prior baseline acceleration scheme, the implemented acceleration scheme achieves 2.4X - 4.3X performance speedup and reduces the energy consumption by 27.7% - 54.5% on a set of realistic benchmarks.

Includes members:
  Member "towards a uniform architecture for the efficient implementation of 2d and 3d deconvolutional neural networks on fpgas":
    also connected through other hyperedges:
      (none)
  Member "ganax a unified mimd simd acceleration for generative adversarial networks":
    also connected through other hyperedges:
      (none)
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "triple generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local facial attribute transfer through inpainting")
  Member "perceptual generative adversarial networks for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "extended feature pyramid network for small object detection")
  Member "gp gan towards realistic high resolution image blending":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a design methodology for efficient implementation of deconvolutional neural networks on an fpga":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an energy efficient fpga based deconvolutional neural networks accelerator for single image super resolution")
  Member "artgan artwork synthesis with conditional categorical gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification")

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.LG(Machine Learning)