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: "tom net learning transparent object matting from a single image"
  Abstract: This paper addresses the problem of transparent object matting. Existing image matting approaches for transparent objects often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we first formulate transparent object matting as a refractive flow estimation problem. We then propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 158K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also collect a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.

Connected through hyperedge HE-1 (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"
    - "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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "cleargrasp 3d shape estimation of transparent objects for manipulation"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "the replica dataset a digital replica of indoor spaces"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "form2fit learning shape priors for generalizable assembly from disassembly"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "learning robotic assembly from cad"
    - "time reversal as self supervision"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "dilated residual networks"

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

Connected through hyperedge HE-5 (induced by source paper "automatically extract the semi transparent motion blurred hand from a single image"):
  with members:
    - "analysis of hand segmentation in the wild"
    - "semantic human matting"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning transparent object matting"):
  with members:
    - "deep image matting"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "colored transparent object matting from a single image using deep learning"):
  with members:
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

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)