human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pvnet pixel wise voting network for 6dof pose estimation"):
  Abstract: This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise unit vectors pointing to the keypoints and use these vectors to vote for keypoint locations using RANSAC. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code will be avaliable at this https URL.

Includes members:
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "when regression meets manifold learning for object recognition and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "6d object pose regression via supervised learning on point clouds")
  Member "making deep heatmaps robust to partial occlusions for 3d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "cut paste and learn surprisingly easy synthesis for instance detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "real time seamless single shot 6d object pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural mesh refiner for 6 dof pose estimation")

Question: Please tell me which class the source paper 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)