human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "deeptof off the shelf real time correction of multipath interference in time of flight imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised deep depth denoising")
  Member "burst denoising with kernel prediction networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")
  Member "recent advances in transient imaging a computer graphics and vision perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non line of sight reconstruction using efficient transient rendering")
  Member "learning to remove multipath distortions in time of flight range images for a robotic arm setup":
    also connected through other hyperedges:
      (none)
  Member "learning the correction for multi path deviations in time of flight cameras":
    also connected through other hyperedges:
      (none)
  Member "flownet learning optical flow with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "sra fast removal of general multipath for tof sensors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "very power efficient neural time of flight")

Question: Please tell me which class the source paper 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)