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: "siamese regression networks with efficient mid level feature extraction for 3d object pose estimation"
  Abstract: In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging problem of regression in angle space indirectly, focusing on learning discriminative features that are later fed into a separate architecture for 3D pose estimation. In contrast, we propose an end-to-end learning framework for directly regressing object poses by exploiting Siamese Networks. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset.

Connected through hyperedge HE-1 (induced by source paper "deepurl deep pose estimation framework for underwater relative localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "beyond sharing weights for deep domain adaptation"
    - "cullnet calibrated and pose aware confidence scores for object pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "yolov3 an incremental improvement"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-3 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"

Connected through hyperedge HE-4 (induced by source paper "6d object pose estimation without pnp"):
  with members:
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "3d pose regression using convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "training a feedback loop for hand pose estimation"
    - "3d bounding box estimation using deep learning and geometry"

Connected through hyperedge HE-5 (induced by source paper "real time object pose estimation with pose interpreter networks"):
  with members:
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "geometric loss functions for camera pose regression with deep learning"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "dilated residual networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered scenes"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"

Connected through hyperedge HE-6 (induced by source paper "3d human pose estimation with siamese equivariant embedding"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "fbi pose towards bridging the gap between 2d images and 3d human poses using forward or backward information"
    - "drpose3d depth ranking in 3d human pose estimation"
    - "it s all relative monocular 3d human pose estimation from weakly supervised data"
    - "exploiting temporal information for 3d human pose estimation"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-7 (induced by source paper "making deep heatmaps robust to partial occlusions for 3d object pose estimation"):
  with members:
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-8 (induced by source paper "sports camera calibration via synthetic data"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "a two point method for ptz camera calibration in sports"
    - "soccer on your tabletop"
    - "backtracking regression forests for accurate camera relocalization"
    - "automated top view registration of broadcast football videos"
    - "play and learn using video games to train computer vision models"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

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)