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: "fast disparity estimation using dense networks"
  Abstract: Disparity estimation is a difficult problem in stereo vision because the correspondence technique fails in images with textureless and repetitive regions. Recent body of work using deep convolutional neural networks (CNN) overcomes this problem with semantics. Most CNN implementations use an autoencoder method; stereo images are encoded, merged and finally decoded to predict the disparity map. In this paper, we present a CNN implementation inspired by dense networks to reduce the number of parameters. Furthermore, our approach takes into account semantic reasoning in disparity estimation. Our proposed network, called DenseMapNet, is compact, fast and can be trained end-to-end. DenseMapNet requires 290k parameters only and runs at 30Hz or faster on color stereo images in full resolution. Experimental results show that DenseMapNet accuracy is comparable with other significantly bigger CNN-based methods.

Connected through hyperedge HE-1 (induced by source paper "fast depth estimation for view synthesis"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "pyramid stereo matching network"
    - "stacked hourglass networks for human pose estimation"
    - "perceptual losses for real time style transfer and super resolution"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "irs a large synthetic indoor robotics stereo dataset for disparity and surface normal estimation"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "semi global stereo matching with surface orientation priors"
    - "single shot object detection with enriched semantics"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "semantic stereo for incidental satellite images"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "segstereo exploiting semantic information for disparity estimation"
    - "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "3d geometry aware semantic labeling of outdoor street scenes"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "memory efficient implementation of densenets"

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)