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: "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
  Abstract: Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive researches have been conducted for solving defocus blur, few researches for motion blur and only solution is to capture with extremely fast shutter speed. In this paper, unlike the previous approaches, we actively utilize motion blur, which we refer to as a light flow, to estimate depth. Analysis reveals that minimum two light flows, which are retrieved from two projected patterns on the object, are required for depth estimation. To retrieve two light flows at the same time, two sets of parallel line patterns are illuminated from two video projectors and the size of motion blur of each line is precisely measured. By analyzing the light flows, i.e. lengths of the blurs, scene depth information is estimated. In the experiments, 3D shapes of fast moving objects, which are inevitably captured with motion blur, are successfully reconstructed by our technique.

Connected through hyperedge HE-1 (induced by source paper "single image depth estimation trained via depth from defocus cues"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "aperture supervision for monocular depth estimation"

Connected through hyperedge HE-2 (induced by source paper "grabar occlusion aware grabbing virtual objects in ar"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "learning to infer the depth map of a hand from its color image"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "deep ordinal regression network for monocular depth estimation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised deep learning for monocular depth map prediction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

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)