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: "highly efficient forward and backward propagation of convolutional neural networks for pixelwise classification"
  Abstract: We present highly efficient algorithms for performing forward and backward propagation of Convolutional Neural Network (CNN) for pixelwise classification on images. For pixelwise classification tasks, such as image segmentation and object detection, surrounding image patches are fed into CNN for predicting the classes of centered pixels via forward propagation and for updating CNN parameters via backward propagation. However, forward and backward propagation was originally designed for whole-image classification. Directly applying it to pixelwise classification in a patch-by-patch scanning manner is extremely inefficient, because surrounding patches of pixels have large overlaps, which lead to a lot of redundant computation. #R##N#The proposed algorithms eliminate all the redundant computation in convolution and pooling on images by introducing novel d-regularly sparse kernels. It generates exactly the same results as those by patch-by-patch scanning. Convolution and pooling operations with such kernels are able to continuously access memory and can run efficiently on GPUs. A fraction of patches of interest can be chosen from each training image for backward propagation by applying a mask to the error map at the last CNN layer. Its computation complexity is constant with respect to the number of patches sampled from the image. Experiments have shown that our proposed algorithms speed up commonly used patch-by-patch scanning over 1500 times in both forward and backward propagation. The speedup increases with the sizes of images and patches.

Connected through hyperedge HE-1 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "smoothed dilated convolutions for improved dense prediction"
    - "contextvp fully context aware video prediction"
    - "active convolution learning the shape of convolution for image classification"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "generative image inpainting with contextual attention"
    - "octnetfusion learning depth fusion from data"
    - "progressive growing of gans for improved quality stability and variation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"

Connected through hyperedge HE-3 (induced by source paper "multi branch convolutional neural network for multiple sclerosis lesion segmentation"):
  with members:
    - "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks"
    - "deepvesselnet vessel segmentation centerline prediction and bifurcation detection in 3 d angiographic volumes"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "brain tumor segmentation with deep neural networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "semantic segmentation via highly fused convolutional network with multiple soft cost functions"):
  with members:
    - "a review on deep learning techniques applied to semantic segmentation"
    - "semantic understanding of scenes through the ade20k dataset"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "video propagation networks"
    - "pyramid scene parsing network"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "contrast oriented deep neural networks for salient object detection"):
  with members:
    - "weakly supervised salient object detection using image labels"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "instance level salient object segmentation"
    - "deep contrast learning for salient object detection"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "visual saliency detection based on multiscale deep cnn features"

Connected through hyperedge HE-6 (induced by source paper "smoothed dilated convolutions for improved dense prediction"):
  with members:
    - "pixel deconvolutional networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "cnn based segmentation of medical imaging data"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning trends for focal brain pathology segmentation in mri"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "identity mappings in deep residual networks"
    - "the importance of skip connections in biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning deep representations for scene labeling with semantic context guided supervision"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "region based semantic segmentation with end to end training"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

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)