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: "a design methodology for efficient implementation of deconvolutional neural networks on an fpga"
  Abstract: Author(s): Zhang, Xinyu | Advisor(s): Kreutz-Delgado, Ken; Smarr, Larry | Abstract: In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors. However, to date, there has been little research on the use of FPGA implementations of deconvolutional neural networks (DCNNs). DCNNs, also known as generative CNNs, encode high-dimensional probability distributions and have been widely used for computer vision applications such as scene completion, scene segmentation, image creation, image denoising, and super-resolution imaging. We propose an FPGA architecture for deconvolutional networks built around an accelerator which effectively handles the complex memory access patterns needed to perform strided deconvolutions, and that supports convolution as well. We also develop a three-step design optimization method that systematically exploits statistical analysis, design space exploration and VLSI optimization. To verify our FPGA deconvolutional accelerator design methodology we train DCNNs offline on two representative datasets using the generative adversarial network method (GAN) run on Tensorflow, and then map these DCNNs to an FPGA DCNN-plus-accelerator implementation to perform generative inference on a Xilinx Zynq-7000 FPGA. Our DCNN implementation achieves a peak performance density of 0.012 GOPs/DSP.

Connected through hyperedge HE-1 (induced by source paper "an energy efficient fpga based deconvolutional neural networks accelerator for single image super resolution"):
  with members:
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "text attentional convolutional neural network for scene text detection"
    - "accelerating the super resolution convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "fixed point quantization of deep convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "accelerating deconvolution on unmodified cnn accelerators for generative adversarial networks a software approach"):
  with members:
    - "towards a uniform architecture for the efficient implementation of 2d and 3d deconvolutional neural networks on fpgas"
    - "ganax a unified mimd simd acceleration for generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "triple generative adversarial nets"
    - "perceptual generative adversarial networks for small object detection"
    - "gp gan towards realistic high resolution image blending"
    - "artgan artwork synthesis with conditional categorical gans"
    - "automatic vertebra labeling in large scale 3d ct using deep image to image network with message passing and sparsity regularization"

Connected through hyperedge HE-3 (induced by source paper "huge2 a highly untangled generative model engine for edge computing"):
  with members:
    - "rethinking atrous convolution for semantic image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "going deeper with convolutions"
    - "conditional generative adversarial nets"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "towards design methodology of efficient fast algorithms for accelerating generative adversarial networks on fpgas"):
  with members:
    - "an energy efficient fpga based deconvolutional neural networks accelerator for single image super resolution"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial 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.LG(Machine Learning)