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: "towards scene understanding with detailed 3d object representations"
  Abstract: Current approaches to semantic image and scene understanding typically employ rather simple object representations such as 2D or 3D bounding boxes. While such coarse models are robust and allow for reliable object detection, they discard much of the information about objects' 3D shape and pose, and thus do not lend themselves well to higher-level reasoning. Here, we propose to base scene understanding on a high-resolution object representation. An object class--in our case cars--is modeled as a deformable 3D wireframe, which enables fine-grained modeling at the level of individual vertices and faces. We augment that model to explicitly include vertex-level occlusion, and embed all instances in a common coordinate frame, in order to infer and exploit object-object interactions. Specifically, from a single view we jointly estimate the shapes and poses of multiple objects in a common 3D frame. A ground plane in that frame is estimated by consensus among different objects, which significantly stabilizes monocular 3D pose estimation. The fine-grained model, in conjunction with the explicit 3D scene model, further allows one to infer part-level occlusions between the modeled objects, as well as occlusions by other, unmodeled scene elements. To demonstrate the benefits of such detailed object class models in the context of scene understanding we systematically evaluate our approach on the challenging KITTI street scene dataset. The experiments show that the model's ability to utilize image evidence at the level of individual parts improves monocular 3D pose estimation w.r.t. both location and (continuous) viewpoint.

Connected through hyperedge HE-1 (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-2 (induced by source paper "monocular 3d object detection with pseudo lidar point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "ipod intensive point based object detector for point cloud"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-4 (induced by source paper "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"):
  with members:
    - "visual inertial object detection and mapping"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-5 (induced by source paper "deep fitting degree scoring network for monocular 3d object detection"):
  with members:
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "3d bounding box estimation using deep learning and geometry"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "vehicle detection from 3d lidar using fully convolutional network"

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 "deep supervision with intermediate concepts"):
  with members:
    - "associative domain adaptation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-8 (induced by source paper "learning to look around objects for top view representations of outdoor scenes"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "cognitive mapping and planning for visual navigation"
    - "semantic scene completion from a single depth image"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual 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)