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 "end to end cad model retrieval and 9dof alignment in 3d scans"):
  Abstract: We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with clean, complete object geometry. Our main contribution lies in formulating a differentiable Procrustes alignment that is paired with a symmetry-aware dense object correspondence prediction. To simultaneously align CAD models to all the objects of a scanned scene, our approach detects object locations, then predicts symmetry-aware dense object correspondences between scan and CAD geometry in a unified object space, as well as a nearest neighbor CAD model, both of which are then used to inform a differentiable Procrustes alignment. Our approach operates in a fully-convolutional fashion, enabling alignment of CAD models to the objects of a scan in a single forward pass. This enables our method to outperform state-of-the-art approaches by $19.04\%$ for CAD model alignment to scans, with $\approx 250\times$ faster runtime than previous data-driven approaches.

Includes members:
  Member "3d local features for direct pairwise registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")
  Member "normalized object coordinate space for category level 6d object pose and size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "planercnn 3d plane detection and reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from planes to corners multi purpose primitive detection in unorganized 3d point clouds")
  Member "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a rotation invariant framework for deep point cloud analysis")
  Member "scan2cad learning cad model alignment in rgb d scans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "imvotenet boosting 3d object detection in point clouds with image votes")
  Member "pix3d dataset and methods for single image 3d shape modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image")
  Member "holistic 3d scene parsing and reconstruction from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "complete 3d scene parsing from an rgbd image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d manhattan room layout reconstruction from a single 360 image")

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)