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: "meshed up learnt error correction in 3d reconstructions"
  Abstract: Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. #R##N#We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.

Connected through hyperedge HE-1 (induced by source paper "learning to correct 3d reconstructions from multiple views"):
  with members:
    - "learning geometrically consistent mesh corrections"
    - "group normalization"
    - "deep depth completion of a single rgb d image"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "deeper depth prediction with fully convolutional residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "dynamic filter networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning geometrically consistent mesh corrections"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep depth completion of a single rgb d image"
    - "propagating confidences through cnns for sparse data regression"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"
    - "sparsity invariant cnns"

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)