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: "tackling 3d tof artifacts through learning and the flat dataset"
  Abstract: Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras. We propose a two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities, and allows to simulate different camera hardware. Using the Kinect 2 camera as a baseline, we show improved reconstruction errors over state-of-the-art methods, on both simulated and real data.

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"
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
    - "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"
    - "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"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-3 (induced by source paper "defogging kinect simultaneous estimation of object region and depth in foggy scenes"):
  with members:
    - "densely connected pyramid dehazing network"
    - "dehazenet an end to end system for single image haze removal"
    - "non local patch regression robust image denoising in patch space"

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)