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: "designing effective inter pixel information flow for natural image matting"
  Abstract: We present a novel, purely affinity-based natural image matting algorithm. Our method relies on carefully defined pixel-to-pixel connections that enable effective use of information available in the image. We control the information flow from the known-opacity regions into the unknown region, as well as within the unknown region itself, by utilizing multiple definitions of pixel affinities. Among other forms of information flow, we introduce color-mixture flow, which builds upon local linear embedding and effectively encapsulates the relation between different pixel opacities. Our resulting novel linear system formulation can be solved in closed-form and is robust against several fundamental challenges of natural matting such as holes and remote intricate structures. Our evaluation using the alpha matting benchmark suggests a significant performance improvement over the current methods. While our method is primarily designed as a standalone matting tool, we show that it can also be used for regularizing mattes obtained by sampling-based methods. We extend our formulation to layer color estimation and show that the use of multiple channels of flow increases the layer color quality. We also demonstrate our performance in green-screen keying and further analyze the characteristics of the affinities used in our method.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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"
    - "deep image matting"

Connected through hyperedge HE-5 (induced by source paper "indices matter learning to index for deep image matting"):
  with members:
    - "alphagan generative adversarial networks for natural image matting"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tom net learning transparent object matting from a single image"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "semantic human matting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cbam convolutional block attention module"
    - "the case for learned index structures"

Connected through hyperedge HE-6 (induced by source paper "context aware image matting for simultaneous foreground and alpha estimation"):
  with members:
    - "alphagan generative adversarial networks for natural image matting"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tom net learning transparent object matting from a single image"
    - "fighting fake news image splice detection via learned self consistency"
    - "the perception distortion tradeoff"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context aware synthesis for video frame interpolation"

Connected through hyperedge HE-7 (induced by source paper "disentangled image matting"):
  with members:
    - "dmcnn dual domain multi scale convolutional neural network for compression artifacts removal"
    - "alphagan generative adversarial networks for natural image matting"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"
    - "semantic human matting"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep image matting"

Connected through hyperedge HE-8 (induced by source paper "semantic human matting"):
  with members:
    - "fast deep matting for portrait animation on mobile phone"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep 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)