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: "exploiting depth from single monocular images for object detection and semantic segmentation"
  Abstract: Augmenting RGB data with measured depth has been shown to improve the performance of a range of tasks in computer vision, including object detection and semantic segmentation. Although depth sensors such as the Microsoft Kinect have facilitated easy acquisition of such depth information, the vast majority of images used in vision tasks do not contain depth information. In this paper, we show that augmenting RGB images with estimated depth can also improve the accuracy of both object detection and semantic segmentation. Specifically, we first exploit the recent success of depth estimation from monocular images and learn a deep depth estimation model. Then, we learn deep depth features from the estimated depth and combine with RGB features for object detection and semantic segmentation. In addition, we propose an RGB-D semantic segmentation method, which applies a multi-task training scheme: semantic label prediction and depth value regression. We test our methods on several data sets and demonstrate that incorporating information from estimated depth improves the performance of object detection and semantic segmentation remarkably.

Connected through hyperedge HE-1 (induced by source paper "incremental scene synthesis"):
  with members:
    - "global pose estimation with an attention based recurrent network"
    - "gibson env real world perception for embodied agents"
    - "spectral normalization for generative adversarial networks"
    - "active neural localization"
    - "learning models for visual 3d localization with implicit mapping"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-2 (induced by source paper "seeing beyond appearance mapping real images into geometrical domains for unsupervised cad based recognition"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "predicting deeper into the future of semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (induced by source paper "learning depth from single images with deep neural network embedding focal length"):
  with members:
    - "richer convolutional features for edge detection"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "sparsity invariant cnns"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-4 (induced by source paper "from selective deep convolutional features to compact binary representations for image retrieval"):
  with members:
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "selective deep convolutional features for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale image search"
    - "learning to hash with binary deep neural network"

Connected through hyperedge HE-5 (induced by source paper "ev segnet semantic segmentation for event based cameras"):
  with members:
    - "realtime time synchronized event based stereo"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "cnn based segmentation of medical imaging data"

Connected through hyperedge HE-6 (induced by source paper "a convolutional feature map based deep network targeted towards traffic detection and classification"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "citypersons a diverse dataset for pedestrian detection"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "attentive contexts for object detection"
    - "deep residual learning for image recognition"

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)