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: "crafting a multi task cnn for viewpoint estimation"
  Abstract: Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation. However different ways of formulating this problem have been proposed and the competing approaches have been explored with very different design choices. This paper presents a comparison of these approaches in a unified setting as well as a detailed analysis of the key factors that impact performance. Followingly, we present a new joint training method with the detection task and demonstrate its benefit. We also highlight the superiority of classification approaches over regression approaches, quantify the benefits of deeper architectures and extended training data, and demonstrate that synthetic data is beneficial even when using ImageNet training data. By combining all these elements, we demonstrate an improvement of approximately 5% mAVP over previous state-of-the-art results on the Pascal3D+ dataset. In particular for their most challenging 24 view classification task we improve the results from 31.1% to 36.1% mAVP.

Connected through hyperedge HE-1 (induced by source paper "pose from shape deep pose estimation for arbitrary 3d objects"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "matching rgb images to cad models for object pose estimation"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dynamic graph cnn for learning on point clouds"
    - "starmap for category agnostic keypoint and viewpoint estimation"

Connected through hyperedge HE-2 (induced by source paper "synthesizing 3d shapes from silhouette image collections using multi projection generative adversarial networks"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "learning category specific mesh reconstruction from image collections"
    - "stabilizing gan training with multiple random projections"
    - "spectral normalization for generative adversarial networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (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-4 (induced by source paper "not only look but observe variational observation model of scene level 3d multi object understanding for probabilistic slam"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "toward predictive machine learning for active vision"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "a variational feature encoding method of 3d object for probabilistic semantic slam"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "3d object reconstruction from a single depth view with adversarial learning"

Connected through hyperedge HE-5 (induced by source paper "conservative wasserstein training for pose estimation"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "deep verifier networks verification of deep discriminative models with deep generative models"
    - "confidence regularized self training"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"

Connected through hyperedge HE-6 (induced by source paper "car pose in context accurate pose estimation with ground plane constraints"):
  with members:
    - "a geometric approach to obtain a bird s eye view from an image"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "groundnet monocular ground plane normal estimation with geometric consistency"

Connected through hyperedge HE-7 (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-8 (induced by source paper "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"):
  with members:
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "viewpoint estimation insights model"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "3d pose regression using convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "3d bounding box estimation using deep learning and geometry"
    - "siamese instance search for tracking"

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)