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: "pixel deconvolutional networks"
  Abstract: Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem. This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map. To address this problem, we propose the pixel deconvolutional layer (PixelDCL) to establish direct relationships among adjacent pixels on the up-sampled feature map. Our method is based on a fresh interpretation of the regular deconvolution operation. The resulting PixelDCL can be used to replace any deconvolutional layer in a plug-and-play manner without compromising the fully trainable capabilities of original models. The proposed PixelDCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick. Experimental results on semantic segmentation demonstrate that PixelDCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than deconvolutional layers. When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.

Connected through hyperedge HE-1 (induced by source paper "pixel level dense prediction without decoder"):
  with members:
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the reconstruction of face images from deep face templates"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "srefi synthesis of realistic example face images"
    - "improved training of wasserstein gans"
    - "synthesizing normalized faces from facial identity features"
    - "memnet a persistent memory network for image restoration"
    - "clustering millions of faces by identity"
    - "do we really need to collect millions of faces for effective face recognition"

Connected through hyperedge HE-3 (induced by source paper "towards corner case detection for autonomous driving"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lidar and camera detection fusion in a real time industrial multi sensor collision avoidance system"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "future frame prediction for anomaly detection a new baseline"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "smoothed dilated convolutions for improved dense prediction"):
  with members:
    - "large scale learnable graph convolutional networks"
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-5 (induced by source paper "convolutional crfs for semantic segmentation"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "understanding deep learning requires rethinking generalization"
    - "pyramid scene parsing network"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-6 (induced by source paper "super resolution using convolutional neural networks without any checkerboard artifacts"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "enhancenet single image super resolution through automated texture synthesis"
    - "deeply recursive convolutional network for image super resolution"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)