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: "sra fast removal of general multipath for tof sensors"
  Abstract: A major issue with Time of Flight sensors is the presence of multipath interference. We present Sparse Reflections Analysis (SRA), an algorithm for removing this interference which has two main advantages. First, it allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on $L_1$ optimization. Second, due to a novel dimension reduction, we are able to produce a very fast version of SRA, which is able to run at frame rate. Experimental results on both synthetic data with ground truth, as well as real images of challenging scenes, validate the approach.

Connected through hyperedge HE-1 (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"
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"

Connected through hyperedge HE-2 (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"
    - "recent advances in transient imaging a computer graphics and vision perspective"
    - "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"

Connected through hyperedge HE-3 (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-4 (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"
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"
    - "improvements of the 3d images captured with time of flight cameras"

Connected through hyperedge HE-5 (induced by source paper "learning to remove multipath distortions in time of flight range images for a robotic arm setup"):
  with members:
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"
    - "caffe convolutional architecture for fast feature embedding"
    - "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)