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: "3d object class detection in the wild"
  Abstract: Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following careful design, in each stage it constantly improves the performance and achieves state-ofthe-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal3D+ dataset.

Connected through hyperedge HE-1 (induced by source paper "monocular 3d object detection with decoupled structured polygon estimation and height guided depth estimation"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-2 (induced by source paper "gp2c geometric projection parameter consensus for joint 3d pose and focal length estimation in the wild"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose estimation for fine grained object categories"
    - "a unified framework for multi view multi class object pose estimation"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-3 (induced by source paper "joint viewpoint and keypoint estimation with real and synthetic data"):
  with members:
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "viewpoint estimation insights model"
    - "3d pose estimation for fine grained object categories"
    - "learning to estimate 3d hand pose from single rgb images"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "6 dof object pose from semantic keypoints"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "learning 3d shape completion under weak supervision"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "3d pose estimation and 3d model retrieval for objects in the wild"):
  with members:
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "6 dof object pose from semantic keypoints"
    - "3d bounding box estimation using deep learning and geometry"
    - "learning a predictable and generative vector representation for objects"
    - "identity mappings in deep residual networks"
    - "crafting a multi task cnn for viewpoint estimation"
    - "pose induction for novel object categories"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "learning to detect vehicles by clustering appearance patterns"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "3d bounding box estimation using deep learning and geometry"):
  with members:
    - "6 dof object pose from semantic keypoints"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "colorful image colorization"
    - "fast single shot detection and pose estimation"
    - "you only look once unified real time object detection"
    - "a coarse to fine model for 3d pose estimation and sub category recognition"

Connected through hyperedge HE-8 (induced by source paper "what is holding back convnets for detection"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "holistically nested edge detection"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "understanding deep image representations by inverting them"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "going deeper with convolutions"

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)