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: "real time dense depth estimation by fusing stereo with sparse depth measurements"
  Abstract: We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera. The goal of this work is to exploit the complementary strengths of the two sensor modalities, the accurate but sparse range measurements and the ambiguous but dense stereo information. These two sources are effectively and efficiently fused by combining ideas from anisotropic diffusion and semi-global matching. #R##N#We evaluate our approach on the KITTI 2015 and Middlebury 2014 datasets, using randomly sampled ground truth range measurements as our sparse depth input. We achieve significant performance improvements with a small fraction of range measurements on both datasets. We also provide qualitative results from our platform using the PMDTec Monstar sensor. Our entire pipeline runs on an NVIDIA TX-2 platform at 5Hz on 1280x1024 stereo images with 128 disparity levels.

Connected through hyperedge HE-1 (induced by source paper "dfinenet ego motion estimation and depth refinement from sparse noisy depth input with rgb guidance"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "fast autonomous flight in gps denied and cluttered environments"

Connected through hyperedge HE-2 (induced by source paper "3d lidar and stereo fusion using stereo matching network with conditional cost volume normalization"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "deepmvs learning multi view stereopsis"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "pyramid stereo matching network"
    - "modulating early visual processing by language"

Connected through hyperedge HE-3 (induced by source paper "deep learning assisted high resolution binocular stereo depth reconstruction"):
  with members:
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "superdepth self supervised super resolved monocular depth estimation"
    - "pyramid stereo matching network"
    - "anytime stereo image depth estimation on mobile devices"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

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)