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 pose estimation for fine grained object categories"
  Abstract: Existing object pose estimation datasets are related to generic object types and there is so far no dataset for fine-grained object categories. In this work, we introduce a new large dataset to benchmark pose estimation for fine-grained objects, thanks to the availability of both 2D and 3D fine-grained data recently. Specifically, we augment two popular fine-grained recognition datasets (StanfordCars and CompCars) by finding a fine-grained 3D CAD model for each sub-category and manually annotating each object in images with 3D pose. We show that, with enough training data, a full perspective model with continuous parameters can be estimated using 2D appearance information alone. We achieve this via a framework based on Faster/Mask R-CNN. This goes beyond previous works on category-level pose estimation, which only estimate discrete/continuous viewpoint angles or recover rotation matrices often with the help of key points. Furthermore, with fine-grained 3D models available, we incorporate a dense 3D representation named as location field into the CNN-based pose estimation framework to further improve the performance. The new dataset is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

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"
    - "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-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"
    - "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"
    - "convolutional pose machines"

Connected through hyperedge HE-4 (induced by source paper "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "deep ordinal regression network for monocular depth estimation"
    - "vehicle pose and shape estimation through multiple monocular vision"
    - "attention is all you need"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "cascaded pyramid network for multi person pose estimation"
    - "3d reconstruction of simple objects from a single view silhouette image"

Connected through hyperedge HE-5 (induced by source paper "part level car parsing and reconstruction from single street view"):
  with members:
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "instance level human parsing via part grouping network"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "improving facial attribute prediction using semantic segmentation"
    - "adversarial discriminative domain adaptation"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

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)