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: "embedded real time stereo estimation via semi global matching on the gpu"
  Abstract: Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640480, 128 disparity levels, and using 4 path directions for the SGM method.

Connected through hyperedge HE-1 (induced by source paper "robust full fov depth estimation in tele wide camera system"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "megadepth learning single view depth prediction from internet photos"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-2 (induced by source paper "fast and differentiable message passing for stereo vision"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "generalized range moves"
    - "memory efficient max flow for multi label submodular mrfs"
    - "conditional random fields as recurrent neural networks"
    - "iteratively reweighted graph cut for multi label mrfs with non convex priors"
    - "loopy belief propagation for approximate inference an empirical study"
    - "learning graphical model parameters with approximate marginal inference"

Connected through hyperedge HE-3 (induced by source paper "real time highly accurate dense depth on a power budget using an fpga cpu hybrid soc"):
  with members:
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "real time dense stereo matching with elas on fpga accelerated embedded devices"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "end to end learning of cost volume aggregation for real time dense stereo"

Connected through hyperedge HE-4 (induced by source paper "rapid light field depth estimation with semi global matching"):
  with members:
    - "a compact light field camera for real time depth estimation"
    - "fast and efficient depth map estimation from light fields"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"

Connected through hyperedge HE-5 (induced by source paper "monocular depth estimation by learning from heterogeneous datasets"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "unsupervised learning of depth and ego motion from video"
    - "a compromise principle in deep monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-6 (induced by source paper "real time dense depth estimation by fusing stereo with sparse depth measurements"):
  with members:
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "high resolution lidar based depth mapping using bilateral filter"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-7 (induced by source paper "the open vision computer an integrated sensing and compute system for mobile robots"):
  with members:
    - "fast autonomous flight in gps denied and cluttered environments"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "pirvs an advanced visual inertial slam system with flexible sensor fusion and hardware co design"

Connected through hyperedge HE-8 (induced by source paper "slanted stixels representing san francisco s steepest streets"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "fully convolutional networks for semantic segmentation"

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)