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: "peeking behind objects layered depth prediction from a single image"
  Abstract: Abstract   While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limits the use of depth prediction in augmented and virtual reality applications, that aim at scene exploration by synthesizing the scene from a different vantage point, or at diminished reality. To address this issue, we shift the focus from conventional depth map prediction to the regression of a specific data representation called Layered Depth Image (LDI), which contains information about the occluded regions in the reference frame and can fill in occlusion gaps in case of small view changes. We propose a novel approach based on Convolutional Neural Networks (CNNs) to jointly predict depth maps and foreground separation masks used to condition Generative Adversarial Networks (GANs) for hallucinating plausible color and depths in the initially occluded areas. We demonstrate the effectiveness of our approach for novel scene view synthesis from a single image.

Connected through hyperedge HE-1 (induced by source paper "3d scene reconstruction with multi layer depth and epipolar transformers"):
  with members:
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "multiresolution tree networks for 3d point cloud processing"
    - "splatnet sparse lattice networks for point cloud processing"
    - "layer structured 3d scene inference via view synthesis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-2 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"

Connected through hyperedge HE-3 (induced by source paper "object driven multi layer scene decomposition from a single image"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep depth completion of a single rgb d image"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "layer structured 3d scene inference via view synthesis"
    - "stereo magnification learning view synthesis using multiplane images"
    - "adversarial semantic scene completion from a single depth image"
    - "segan segmenting and generating the invisible"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "pushing the boundaries of view extrapolation with multiplane images"):
  with members:
    - "layer structured 3d scene inference via view synthesis"
    - "spectral normalization for generative adversarial networks"
    - "stereo magnification learning view synthesis using multiplane images"
    - "generative image inpainting with contextual attention"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "photographic image synthesis with cascaded refinement networks"
    - "learning based view synthesis for light field cameras"

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)