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: "fast single shot detection and pose estimation"
  Abstract: For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection. Many approaches to pose estimation rely on detecting or tracking parts or keypoints [11, 21]. In this paper we build on a recent state-of-the-art convolutional network for slidingwindow detection [10] to provide detection and rough pose estimation in a single shot, without intermediate stages of detecting parts or initial bounding boxes. While not the first system to treat pose estimation as a categorization problem, this is the first attempt to combine detection and pose estimation at the same level using a deep learning approach. The key to the architecture is a deep convolutional network where scores for the presence of an object category, the offset for its location, and the approximate pose are all estimated on a regular grid of locations in the image. The resulting system is as accurate as recent work on pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly faster (46 frames per second (FPS) on a TITAN X GPU). This approach to detection and rough pose estimation is fast and accurate enough to be widely applied as a pre-processing step for tasks including high-accuracy pose estimation, object tracking and localization, and vSLAM.

Connected through hyperedge HE-1 (induced by source paper "object 6d pose estimation with non local attention"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "non local neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "3d bounding box estimation using deep learning and geometry"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"

Connected through hyperedge HE-2 (induced by source paper "towards robust learning based pose estimation of noncooperative spacecraft"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "estimating 6d pose from localizing designated surface keypoints"

Connected through hyperedge HE-3 (induced by source paper "automated focal loss for image based object detection"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"
    - "3d bounding box estimation using deep learning and geometry"

Connected through hyperedge HE-4 (induced by source paper "imapper interaction guided scene mapping from monocular videos"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "factoring shape pose and layout from the 2d image of a 3d scene"

Connected through hyperedge HE-5 (induced by source paper "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "residual codean autoencoder for facial attribute analysis"
    - "single shot temporal action detection"
    - "single shot text detector with regional attention"
    - "scale aware face detection"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-6 (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"
    - "3d pose estimation for fine grained object categories"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning local rgb to cad correspondences for object pose estimation"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "bop benchmark for 6d object pose estimation"
    - "domain transfer for 3d pose estimation from color images without manual annotations"
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose regression using convolutional neural networks"
    - "seethrough finding chairs in heavily occluded indoor scene images"

Connected through hyperedge HE-8 (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"
    - "3d pose estimation for fine grained object categories"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "improving facial attribute prediction using semantic segmentation"
    - "adversarial discriminative domain adaptation"

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)