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: "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"
  Abstract: We demonstrate the use of shape-from-shading (SfS) to improve both the quality and the robustness of 3D reconstruction of dynamic objects captured by a single camera. Unlike previous approaches that made use of SfS as a post-processing step, we offer a principled integrated approach that solves dynamic object tracking and reconstruction and SfS as a single unified cost function. Moving beyond Lambertian S f S , we propose a general approach that models both specularities and shading while simultaneously tracking and reconstructing general dynamic objects. Solving these problems jointly prevents the kinds of tracking failures which can not be recovered from by pipeline approaches. We show state-of-the-art results both qualitatively and quantitatively.

Connected through hyperedge HE-1 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "3d aware scene manipulation via inverse graphics"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "generative image inpainting with contextual attention"
    - "octnetfusion learning depth fusion from data"
    - "progressive growing of gans for improved quality stability and variation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "the visual centrifuge model free layered video representations"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "data augmentation by pairing samples for images classification"
    - "learning to extract a video sequence from a single motion blurred image"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "a short note about kinetics 600"
    - "single image reflection removal using deep encoder decoder network"
    - "end to end speech separation with unfolded iterative phase reconstruction"
    - "single image reflection separation with perceptual losses"

Connected through hyperedge HE-4 (induced by source paper "hdm net monocular non rigid 3d reconstruction with learned deformation model"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "octnetfusion learning depth fusion from data"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"
    - "end to end 3d face reconstruction with deep neural networks"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "unrestricted facial geometry reconstruction using image to image translation"

Connected through hyperedge HE-5 (induced by source paper "deep shape from template wide baseline dense and fast registration and deformable reconstruction from a single image"):
  with members:
    - "geometry aware network for non rigid shape prediction from a single view"
    - "hdm net monocular non rigid 3d reconstruction with learned deformation model"
    - "densepose dense human pose estimation in the wild"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "accurate 3d reconstruction of dynamic scenes from monocular image sequences with severe occlusions"):
  with members:
    - "dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture"
    - "on the closed form solution of the rotation matrix arising in computer vision problems"

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)