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: "bayesian time of flight for realtime shape illumination and albedo"
  Abstract: We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. #R##N#To address this challenge we propose a generative probabilistic model that accurately relates latent imaging conditions to observed camera responses. While principled, realtime inference in the model turns out to be infeasible, and we propose to employ efficient non-parametric regression trees to approximate the model outputs. As a result we are able to provide, for each pixel, at video frame rate, estimates and uncertainty for depth, effective albedo, and ambient light intensity. These results we present are state-of-the-art in depth imaging. #R##N#The flexibility of our approach allows us to easily enrich our generative model. We demonstrate that by extending the original single-path model to a two-path model, capable of describing some multipath effects. The new model is seamlessly integrated in the system at no additional computational cost. #R##N#Our work also addresses the important question of optimal exposure design in pulsed TOF systems. Finally, for benchmark purposes and to obtain realistic empirical priors of multipath and insights into this phenomena, we propose a physically accurate simulation of multipath phenomena.

Connected through hyperedge HE-1 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction"):
  with members:
    - "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather"
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "learning single camera depth estimation using dual pixels"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "uncertainty estimation in one stage object detection"
    - "seeing through fog without seeing fog deep sensor fusion in the absence of labeled training data"
    - "gated2depth real time dense lidar from gated images"

Connected through hyperedge HE-2 (induced by source paper "gated2depth real time dense lidar from gated images"):
  with members:
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "rethinking monocular depth estimation with adversarial training"
    - "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"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "estimating depth from rgb and sparse sensing"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-3 (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"
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

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"
    - "resolving multipath interference in time of flight imaging via modulation frequency diversity and sparse regularization"
    - "sra fast removal of general multipath for tof sensors"
    - "improvements of the 3d images captured with time of flight cameras"

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)