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 robust monocular depth estimation for obstacle detection with fully convolutional networks"
  Abstract: Obstacle Detection is a central problem for any robotic system, and critical for autonomous systems that travel at high speeds in unpredictable environment. This is often achieved through scene depth estimation, by various means. When fast motion is considered, the detection range must be longer enough to allow for safe avoidance and path planning. Current solutions often make assumption on the motion of the vehicle that limit their applicability, or work at very limited ranges due to intrinsic constraints. We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~300Hz), without making assumptions on the type of motion. We achieve these results using a Deep Neural Network approach trained on real and synthetic images and trading some depth accuracy for fast, robust and consistent operation. We show how photo-realistic synthetic images are able to solve the problem of training set dimension and variety typical of machine learning approaches, and how our system is robust to massive blurring of test images.

Connected through hyperedge HE-1 (induced by source paper "fastdepth fast monocular depth estimation on embedded systems"):
  with members:
    - "deeperlab single shot image parser"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (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"
    - "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"
    - "embedded real time stereo estimation via semi global matching on the gpu"

Connected through hyperedge HE-3 (induced by source paper "anytime stereo image depth estimation on mobile devices"):
  with members:
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"
    - "self supervised siamese learning on stereo image pairs for depth estimation in robotic surgery"
    - "learning affinity via spatial propagation networks"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-4 (induced by source paper "memory based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge"):
  with members:
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "unsupervised learning of depth and ego motion from video"
    - "regret minimization for partially observable deep reinforcement learning"
    - "semi supervised deep learning for monocular depth map prediction"
    - "temporal attention gated model for robust sequence classification"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "fusion of stereo and still monocular depth estimates in a self supervised learning context"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "persistent self supervised learning principle from stereo to monocular vision for obstacle avoidance"
    - "very deep convolutional networks for large scale image recognition"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-6 (induced by source paper "sparse to dense depth prediction from sparse depth samples and a single image"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "sparse depth sensing for resource constrained robots"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "deeper depth prediction with fully convolutional residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "fast accurate thin structure obstacle detection for autonomous mobile robots"):
  with members:
    - "lost and found detecting small road hazards for self driving vehicles"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
    - "direct sparse odometry"
    - "vision and learning for deliberative monocular cluttered flight"
    - "pushbroom stereo for high speed navigation in cluttered environments"

Connected through hyperedge HE-8 (induced by source paper "automatic model based dataset generation for fast and accurate crop and weeds detection"):
  with members:
    - "playing for data ground truth from computer games"
    - "semantic instance annotation of street scenes by 3d to 2d label transfer"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "deep plant plant identification with convolutional neural networks"
    - "how transferable are features in deep neural 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.RO(Robotics)