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 adaptive convolutional neural networks"
  Abstract: Convolutions are the fundamental building block of CNNs. The fact that their weights are spatially shared is one of the main reasons for their widespread use, but it also is a major limitation, as it makes convolutions content agnostic. We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are multiplied with a spatially-varying kernel that depends on learnable, local pixel features. PAC is a generalization of several popular filtering techniques and thus can be used for a wide range of use cases. Specifically, we demonstrate state-of-the-art performance when PAC is used for deep joint image upsampling. PAC also offers an effective alternative to fully-connected CRF (Full-CRF), called PAC-CRF, which performs competitively, while being considerably faster. In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.

Connected through hyperedge HE-1 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf"):
  with members:
    - "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "relation network for multi label aerial image classification"
    - "building footprint generation using improved generative adversarial networks"
    - "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"

Connected through hyperedge HE-2 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "cfsnet toward a controllable feature space for image restoration"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "image super resolution by neural texture transfer"
    - "a general decoupled learning framework for parameterized image operators"
    - "a benchmark for edge preserving image smoothing"
    - "multiple description convolutional neural networks for image compression"

Connected through hyperedge HE-3 (induced by source paper "few shot video to video synthesis"):
  with members:
    - "video generation from single semantic label map"
    - "dance dance generation motion transfer for internet videos"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "vid2game controllable characters extracted from real world videos"
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "learning to predict layout to image conditional convolutions for semantic image synthesis"):
  with members:
    - "meta sr a magnification arbitrary network for super resolution"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "dynamic filtering with large sampling field for convnets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"

Connected through hyperedge HE-5 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "learning guided convolutional network for depth completion"
    - "blade filter learning for general purpose computational photography"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "joint image filtering with deep convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "unpaired image translation via adaptive convolution based normalization"):
  with members:
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "conditional image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "instagan instance aware image to image translation"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "radius adaptive convolutional neural network"):
  with members:
    - "searching for mobilenetv3"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "s3pool pooling with stochastic spatial sampling"

Connected through hyperedge HE-8 (induced by source paper "pixel adaptive filtering units"):
  with members:
    - "iterative joint image demosaicking and denoising using a residual denoising network"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "neural nearest neighbors networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "blade filter learning for general purpose computational photography"
    - "convolutional networks with adaptive inference graphs"
    - "enhanced deep residual networks for single image super resolution"
    - "burst denoising with kernel prediction 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.CV(Computer Vision and Pattern Recognition)