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: "confidence driven tgv fusion"
  Abstract: We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values. The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data. We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimization problem and their convergence. The performance of the proposed model is evaluated considering the problem of depth image fusion by using both synthetic and real data from publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "improved stereo matching with constant highway networks and reflective confidence learning"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning of deep metrics for stereo reconstruction"):
  with members:
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "the fast bilateral solver"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "low level vision by consensus in a spatial hierarchy of regions"

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)