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: "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
  Abstract: Time-of-flight (ToF) imaging has become a widespread technique for depth estimation, allowing affordable off-the-shelf cameras to provide depth maps in real time. However, multipath interference (MPI) resulting from indirect illumination significantly degrades the captured depth. Most previous works have tried to solve this problem by means of complex hardware modifications or costly computations. In this work, we avoid these approaches and propose a new technique to correct errors in depth caused by MPI, which requires no camera modifications and takes just 10 milliseconds per frame. Our observations about the nature of MPI suggest that most of its information is available in image space; this allows us to formulate the depth imaging process as a spatially-varying convolution and use a convolutional neural network to correct MPI errors. Since the input and output data present similar structure, we base our network on an autoencoder, which we train in two stages. First, we use the encoder (convolution filters) to learn a suitable basis to represent MPI-corrupted depth images; then, we train the decoder (deconvolution filters) to correct depth from synthetic scenes, generated by using a physically-based, time-resolved renderer. This approach allows us to tackle a key problem in ToF, the lack of ground-truth data, by using a large-scale captured training set with MPI-corrupted depth to train the encoder, and a smaller synthetic training set with ground truth depth to train the decoder stage of the network. We demonstrate and validate our method on both synthetic and real complex scenarios, using an off-the-shelf ToF camera, and with only the captured, incorrect depth as input.

Connected through hyperedge HE-1 (induced by source paper "self supervised deep depth denoising"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "a low cost flexible and portable volumetric capturing system"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "noise2void learning denoising from single noisy images"
    - "layer structured 3d scene inference via view synthesis"
    - "robust intrinsic and extrinsic calibration of rgb d cameras"
    - "tackling 3d tof artifacts through learning and the flat dataset"
    - "cnn slam real time dense monocular slam with learned depth prediction"

Connected through hyperedge HE-2 (induced by source paper "deep end to end alignment and refinement for time of flight rgb d module"):
  with members:
    - "deep rigid instance scene flow"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "single view stereo matching"
    - "tackling 3d tof artifacts through learning and the flat dataset"
    - "dsr direct self rectification for uncalibrated dual lens cameras"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-3 (induced by source paper "direct object recognition without line of sight using optical coherence"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "lensless computational imaging through deep learning"
    - "exploit imaging through opaque wall via deep learning"
    - "selective classification for deep neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "depth reconstruction of translucent objects from a single time of flight camera using deep residual networks"):
  with members:
    - "photometric stereo in participating media considering shape dependent forward scatter"
    - "learned multi patch similarity"
    - "visual attribute transfer through deep image analogy"
    - "single image depth estimation by dilated deep residual convolutional neural network and soft weight sum inference"
    - "estimating depth from monocular images as classification using deep fully convolutional residual networks"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "tackling 3d tof artifacts through learning and the flat dataset"):
  with members:
    - "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"
    - "sra fast removal of general multipath for tof sensors"

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.GR(Graphics)