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: "sdf man semi supervised disparity fusion with multi scale adversarial networks"
  Abstract: Fusing disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships between neighboring pixels limit the accuracy and robustness of the existing methods and there is no common method for depth fusion of different kind of data. In this paper, we introduce a method to incorporate supplementary information (intensity, gradient constraints etc.) into a Generative Adversarial Network to better refine each input disparity value. By adopting a multi-scale strategy, the disparity relationship in the fused disparity map is a better estimate of the real distribution. The approach includes a more robust object function to avoid blurry edges, impaints invalid disparity values and requires much fewer ground data to train. The algorithm can be generalized to different kinds of depth fusion. The experiments were conducted through simulation and real data, exploring different fusion opportunities: stereo-monocular fusion from coarse input, stereo-stereo fusion from moderately accurate input, fusion from accurate binocular input and Time of Flight sensor. The experiments show the superiority of the proposed algorithm compared with the most recent algorithms on the public Scene Flow and SYNTH3 datasets. The code is available from this https URL

Connected through hyperedge HE-1 (induced by source paper "udfnet unsupervised disparity fusion with adversarial networks"):
  with members:
    - "pyramid stereo matching network"
    - "improved training of wasserstein gans"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)