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 "hierarchy denoising recursive autoencoders for 3d scene layout prediction"):
  Abstract: Indoor scenes exhibit rich hierarchical structure in 3D object layouts. Many tasks in 3D scene understanding can benefit from reasoning jointly about the hierarchical context of a scene, and the identities of objects. We present a variational denoising recursive autoencoder (VDRAE) that generates and iteratively refines a hierarchical representation of 3D object layouts, interleaving bottom-up encoding for context aggregation and top-down decoding for propagation. We train our VDRAE on large-scale 3D scene datasets to predict both instance-level segmentations and a 3D object detections from an over-segmentation of an input point cloud. We show that our VDRAE improves object detection performance on real-world 3D point cloud datasets compared to baselines from prior work.

Includes members:
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "human centric indoor scene synthesis using stochastic grammar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast 3d indoor scene synthesis with discrete and exact layout pattern extraction")
  Member "birdnet a 3d object detection framework from lidar information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "complex yolo real time 3d object detection on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "single stage object detection from top view grid maps on custom sensor setups")
  Member "recurrent slice networks for 3d segmentation on point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation":
    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 "sgpn similarity group proposal network for 3d point cloud instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

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)