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: "deep image matting"
  Abstract: Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems. Our deep model has two parts. The first part is a deep convolutional encoder-decoder network that takes an image and the corresponding trimap as inputs and predict the alpha matte of the image. The second part is a small convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values and sharper edges. In addition, we also create a large-scale image matting dataset including 49300 training images and 1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images. Experimental results clearly demonstrate the superiority of our algorithm over previous methods.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (induced by source paper "natural image matting via guided contextual attention"):
  with members:
    - "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"
    - "indices matter learning to index for deep image matting"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "alphagan generative adversarial networks for natural image matting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"

Connected through hyperedge HE-3 (induced by source paper "getting to 99 accuracy in interactive segmentation"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "weight standardization"
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "group normalization"
    - "interactive video object segmentation in the wild"
    - "iteratively trained interactive segmentation"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "f b alpha matting"):
  with members:
    - "natural image matting via guided contextual attention"
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "weight standardization"
    - "indices matter learning to index for deep image matting"
    - "context aware image matting for simultaneous foreground and alpha estimation"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"

Connected through hyperedge HE-5 (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"
    - "fast deep matting for portrait animation on mobile phone"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "background matting"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "free form video inpainting with 3d gated convolution and temporal patchgan"
    - "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"
    - "indices matter learning to index for deep image matting"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-7 (induced by source paper "syn2real forgery classification via unsupervised domain adaptation"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "deep visual domain adaptation a survey"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "deep domain confusion maximizing for domain invariance"

Connected through hyperedge HE-8 (induced by source paper "index network"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "deeperlab single shot image parser"
    - "indices matter learning to index for deep image matting"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to see in the dark"
    - "cbam convolutional block attention module"

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)