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: "a mixed classification regression framework for 3d pose estimation from 2d images"
  Abstract: 3D pose estimation from a single 2D image is an important and challenging task in computer vision with applications in autonomous driving, robot manipulation and augmented reality. Since 3D pose is a continuous quantity, a natural formulation for this task is to solve a pose regression problem. However, since pose regression methods return a single estimate of the pose, they have difficulties handling multimodal pose distributions (e.g. in the case of symmetric objects). An alternative formulation, which can capture multimodal pose distributions, is to discretize the pose space into bins and solve a pose classification problem. However, pose classification methods can give large pose estimation errors depending on the coarseness of the discretization. In this paper, we propose a mixed classification-regression framework that uses a classification network to produce a discrete multimodal pose estimate and a regression network to produce a continuous refinement of the discrete estimate. The proposed framework can accommodate different architectures and loss functions, leading to multiple classification-regression models, some of which achieve state-of-the-art performance on the challenging Pascal3D+ dataset.

Connected through hyperedge HE-1 (induced by source paper "a multi hypothesis approach to color constancy"):
  with members:
    - "sensor independent illumination estimation for dnn models"
    - "color cerberus"
    - "what else can fool deep learning addressing color constancy errors on deep neural network performance"
    - "fast fourier color constancy and grayness index for ispa illumination estimation challenge"
    - "convolutional mean a simple convolutional neural network for illuminant estimation"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "unsupervised learning for color constancy"

Connected through hyperedge HE-2 (induced by source paper "semi local 3d lane detection and uncertainty estimation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "deep multi sensor lane detection"
    - "learning lightweight lane detection cnns by self attention distillation"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "evaluating and calibrating uncertainty prediction in regression tasks"
    - "monoloco monocular 3d pedestrian localization and uncertainty estimation"
    - "towards end to end lane detection an instance segmentation approach"
    - "3d lanenet end to end 3d multiple lane detection"

Connected through hyperedge HE-3 (induced by source paper "convolutional mesh regression for single image human shape reconstruction"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human body reconstruction from a single image via volumetric regression"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-4 (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"
    - "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"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-5 (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"
    - "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"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-6 (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"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "click here human localized keypoints as guidance for viewpoint estimation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "marr revisited 2d 3d alignment via surface normal prediction"

Connected through hyperedge HE-8 (induced by source paper "learning a disentangled embedding for monocular 3d shape retrieval and pose estimation"):
  with members:
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "on the continuity of rotation representations in neural networks"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "a unified framework for multi view multi class object pose estimation"
    - "3d pose regression using convolutional 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.CV(Computer Vision and Pattern Recognition)