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: "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"
  Abstract: Time-of-flight (ToF) cameras calculate depth maps by reconstructing phase shifts of amplitude-modulated signals. For broad illumination of transparent objects, reflections from multiple scene points can illuminate a given pixel, giving rise to an erroneous depth map. We report here a sparsity-regularized solution that separates K interfering components using multiple modulation frequency measurements. The method maps ToF imaging to the general framework of spectral estimation theory and has applications in improving depth profiles and exploiting multiple scattering.

Connected through hyperedge HE-1 (induced by source paper "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"):
  with members:
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "free form image inpainting with gated convolution"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-2 (induced by source paper "activestereonet end to end self supervised learning for active stereo systems"):
  with members:
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "intel realsense stereoscopic depth cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "very power efficient neural time of flight"):
  with members:
    - "learning to see in the dark"
    - "deep convolutional denoising of low light images"
    - "least squares generative adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "sra fast removal of general multipath for tof sensors"

Connected through hyperedge HE-4 (induced by source paper "deeptof off the shelf real time correction of multipath interference in time of flight imaging"):
  with members:
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "learning based view synthesis for light field cameras"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "position tracking for virtual reality using commodity wifi"):
  with members:
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "wi fi gesture recognition on existing devices"

Connected through hyperedge HE-6 (induced by source paper "sampling without time recovering echoes of light via temporal phase retrieval"):
  with members:
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "estimation of sparse mimo channels with common support"

Connected through hyperedge HE-7 (induced by source paper "recent advances in transient imaging a computer graphics and vision perspective"):
  with members:
    - "optically lightweight tracking of objects around a corner"
    - "frequency domain tof encoding object depth in modulation frequency"
    - "spatial phase sweep increasing temporal resolution of transient imaging using a light source array"
    - "bayesian time of flight for realtime shape illumination and albedo"
    - "sra fast removal of general multipath for tof sensors"
    - "improvements of the 3d images captured with time of flight cameras"

Connected through hyperedge HE-8 (induced by source paper "learning to remove multipath distortions in time of flight range images for a robotic arm setup"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"
    - "sra fast removal of general multipath for tof sensors"
    - "learning joint intensity depth sparse representations"

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)