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: "recent advances in transient imaging a computer graphics and vision perspective"
  Abstract: Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.

Connected through hyperedge HE-1 (induced by source paper "tackling 3d tof artifacts through learning and the flat dataset"):
  with members:
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
    - "burst denoising with kernel prediction networks"
    - "learning to remove multipath distortions in time of flight range images for a robotic arm setup"
    - "learning the correction for multi path deviations in time of flight cameras"
    - "flownet learning optical flow with convolutional networks"
    - "sra fast removal of general multipath for tof sensors"

Connected through hyperedge HE-2 (induced by source paper "non line of sight reconstruction using efficient transient rendering"):
  with members:
    - "non line of sight imaging with partial occluders and surface normals"
    - "robust non line of sight imaging with single photon detectors"
    - "fast back projection for non line of sight reconstruction"
    - "optically lightweight tracking of objects around a corner"

Connected through hyperedge HE-3 (induced by source paper "progressive transient photon beams"):
  with members:
    - "fast back projection for non line of sight reconstruction"
    - "optically lightweight tracking of objects around a corner"

Connected through hyperedge HE-4 (induced by source paper "virtual wave optics for non line of sight imaging"):
  with members:
    - "fast back projection for non line of sight reconstruction"
    - "spatial phase sweep increasing temporal resolution of transient imaging using a light source array"

Connected through hyperedge HE-5 (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"
    - "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"
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"

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)