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: "convolutional neural networks for joint object detection and pose estimation a comparative study"
  Abstract: In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose. We identify different feature representations of oriented objects, and energies that lead a network to learn this representations. The choice of the representation is crucial since the pose of an object has a natural, continuous structure while its category is a discrete variable. We evaluate the different approaches on the joint object detection and pose estimation task of the Pascal3D+ benchmark using Average Viewpoint Precision. We show that a classification approach on discretized viewpoints achieves state-of-the-art performance for joint object detection and pose estimation, and significantly outperforms existing baselines on this benchmark.

Connected through hyperedge HE-1 (induced by source paper "a mixed classification regression framework for 3d pose estimation from 2d images"):
  with members:
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "3d pose regression using convolutional neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "3d bounding box estimation using deep learning and geometry"
    - "identity mappings in deep residual networks"
    - "crafting a multi task cnn for viewpoint estimation"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-2 (induced by source paper "convolutional networks for object category and 3d pose estimation from 2d images"):
  with members:
    - "the challenge of simultaneous object detection and pose estimation a comparative study"
    - "a unified framework for multi view multi class object pose estimation"
    - "3d pose regression using convolutional neural networks"
    - "6 dof object pose from semantic keypoints"
    - "designing deep convolutional neural networks for continuous object orientation estimation"
    - "3d bounding box estimation using deep learning and geometry"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "6 dof object pose from semantic keypoints"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "global hypothesis generation for 6d object pose estimation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "recovering 6d object pose and predicting next best view in the crowd"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-4 (induced by source paper "crafting a multi task cnn for viewpoint estimation"):
  with members:
    - "learning to segment object candidates"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "viewpoints and keypoints"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"):
  with members:
    - "inferring 3d object pose in rgb d images"
    - "shapenet an information rich 3d model repository"
    - "exploring invariances in deep convolutional neural networks using synthetic images"
    - "3d shapenets a deep representation for volumetric shapes"
    - "viewpoints and keypoints"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "recognizing image style"

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)