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: "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
  Abstract: Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D. The existing methods either are ineffective or only tackle the problem partially. In this paper, we propose an end-to-end model that simultaneously solves all three tasks in real-time given only a single RGB image. The essence of the proposed method is to improve the prediction by i) parametrizing the targets (e.g., 3D boxes) instead of directly estimating the targets, and ii) cooperative training across different modules in contrast to training these modules individually. Specifically, we parametrize the 3D object bounding boxes by the predictions from several modules, i.e., 3D camera pose and object attributes. The proposed method provides two major advantages: i) The parametrization helps maintain the consistency between the 2D image and the 3D world, thus largely reducing the prediction variances in 3D coordinates. ii) Constraints can be imposed on the parametrization to train different modules simultaneously. We call these constraints "cooperative losses" as they enable the joint training and inference. We employ three cooperative losses for 3D bounding boxes, 2D projections, and physical constraints to estimate a geometrically consistent and physically plausible 3D scene. Experiments on the SUN RGB-D dataset shows that the proposed method significantly outperforms prior approaches on 3D object detection, 3D layout estimation, 3D camera pose estimation, and holistic scene understanding.

Connected through hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image"):
  with members:
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "learning to infer and execute 3d shape programs"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "few shot generalization for single image 3d reconstruction via priors"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-2 (induced by source paper "shallow2deep indoor scene modeling by single image understanding"):
  with members:
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "a simple neural network module for relational reasoning"
    - "roomnet end to end room layout estimation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "instaboost boosting instance segmentation via probability map guided copy pasting"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "explicit shape encoding for real time instance segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning segmentation masks with the independence prior"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchy denoising recursive autoencoders for 3d scene layout prediction"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "birdnet a 3d object detection framework from lidar information"
    - "complex yolo real time 3d object detection on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "perspectivenet 3d object detection from a single rgb image via perspective points"):
  with members:
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "visual object networks image generation with disentangled 3d representation"
    - "3d aware scene manipulation via inverse graphics"
    - "learning to reconstruct shapes from unseen classes"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-6 (induced by source paper "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "learning human object interactions by graph parsing neural networks"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning to detect human object interactions"

Connected through hyperedge HE-7 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "roomnet end to end room layout estimation"

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)