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: "6d object detection and next best view prediction in the crowd"
  Abstract: 6D object detection and pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve 6D object pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.

Connected through hyperedge HE-1 (induced by source paper "estimating 3d motion and forces of person object interactions from monocular video"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "end to end recovery of human shape and pose"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"

Connected through hyperedge HE-2 (induced by source paper "homebreweddb rgb d dataset for 6d pose estimation of 3d objects"):
  with members:
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bop benchmark for 6d object pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "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"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "ipose instance aware 6d pose estimation of partly occluded objects"):
  with members:
    - "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"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "ssd single shot multibox detector"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "hashmod a hashing method for scalable 3d object detection"

Connected through hyperedge HE-4 (induced by source paper "ssd 6d making rgb based 3d detection and 6d pose estimation great again"):
  with members:
    - "lift learned invariant feature transform"
    - "3d bounding box estimation using deep learning and geometry"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "hashmod a hashing method for scalable 3d object detection"
    - "feature pyramid networks for object detection"
    - "fast single shot detection and pose estimation"

Connected through hyperedge HE-5 (induced by source paper "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"):
  with members:
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "training a feedback loop for hand pose estimation"
    - "hashmod a hashing method for scalable 3d object detection"
    - "going further with point pair features"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "iterative hough forest with histogram of control points for 6 dof object registration from depth images"):
  with members:
    - "latent class hough forests for 6 dof object pose estimation"
    - "recovering 6d object pose and predicting next best view in the crowd"

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)