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: "semantic human matting"
  Abstract: Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes them difficult to be applied to large scale data or time-sensitive scenarios. In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints learned from data and propose an automatic human matting algorithm (SHM). SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and fine details is challenging. We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha matte. We also construct a very large dataset with high quality annotations consisting of 35,513 unique foregrounds to facilitate the learning and evaluation of human matting. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods.

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"
    - "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-2 (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"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cbam convolutional block attention module"
    - "the case for learned index structures"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (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"
    - "fast end to end trainable guided filter"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"

Connected through hyperedge HE-4 (induced by source paper "towards real time automatic portrait matting on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast deep matting for portrait animation on mobile phone"
    - "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-5 (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"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "fast deep matting for portrait animation on mobile phone"
    - "real time deep hair matting on mobile devices"
    - "deep image matting"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "designing effective inter pixel information flow for natural image matting"

Connected through hyperedge HE-7 (induced by source paper "automatically extract the semi transparent motion blurred hand from a single image"):
  with members:
    - "tom net learning transparent object matting from a single image"
    - "analysis of hand segmentation in the wild"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning depth from single images with deep neural network embedding focal length"
    - "deep image matting"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)