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: "indices matter learning to index for deep image matting"
  Abstract: We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation. By looking at the indices as a function of the feature map, we introduce the concept of learning to index, and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without the need of supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically predicts indices given an input. Due to its flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf convolutional networks that have coupled downsampling and upsampling stages. #R##N#We demonstrate the effectiveness of IndexNet on the task of natural image matting where the quality of learned indices can be visually observed from predicted alpha mattes. Results on the Composition-1k matting dataset show that our model built on MobileNetv2 exhibits at least $16.1\%$ improvement over the seminal VGG-16 based deep matting baseline, with less training data and lower model capacity. Code and models has been made available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "alignseg feature aligned segmentation networks"):
  with members:
    - "gated scnn gated shape cnns for semantic segmentation"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "unified perceptual parsing for scene understanding"

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"
    - "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"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (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"
    - "context aware image matting for simultaneous foreground and alpha estimation"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "group normalization"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep image matting"

Connected through hyperedge HE-5 (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"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "deep image matting"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-6 (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"
    - "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"
    - "the case for learned index structures"

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)