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: "fast deep matting for portrait animation on mobile phone"
  Abstract: Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.

Connected through hyperedge HE-1 (induced by source paper "alphanet an attention guided deep network for automatic image matting"):
  with members:
    - "retrieving similar e commerce images using deep learning"
    - "instance segmentation based semantic matting for compositing applications"
    - "indices matter learning to index for deep image matting"
    - "group normalization"
    - "semantic human matting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "deep image matting"

Connected through hyperedge HE-2 (induced by source paper "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"):
  with members:
    - "boundary aware network for fast and high accuracy portrait segmentation"
    - "alphagan generative adversarial networks for natural image matting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "spectral normalization for generative adversarial networks"
    - "semantic human matting"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "towards real time automatic portrait matting on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "semantic human matting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep image matting"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "boundary aware network for fast and high accuracy portrait segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "semantic human matting"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "real time deep hair matting on mobile devices"
    - "deep image matting"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "synthetic depth of field with a single camera mobile phone"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "deep image matting"
    - "towards accurate multi person pose estimation in the wild"
    - "pose2instance harnessing keypoints for person instance segmentation"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "alphagan generative adversarial networks for natural image matting"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep image matting"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "generative visual manipulation on the natural image manifold"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "semantic human matting"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep image matting"
    - "designing effective inter pixel information flow for natural image matting"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "synthetic data for text localisation in natural images"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)