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 "structured3d a large photo realistic dataset for structured 3d modeling"):
  Abstract: Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often obtained via human labor, which is particularly challenging and inefficient for such tasks due to the large number of 3D structure instances (e.g., line segments) and other factors such as viewpoints and occlusions. In this paper, we present a new synthetic dataset, Structured3D, with the aim to providing large-scale photo-realistic images with rich 3D structure annotations for a wide spectrum of structured 3D modeling tasks. We take advantage of the availability of millions of professional interior designs and automatically extract 3D structures from them. We generate high-quality images with an industry-leading rendering engine. We use our synthetic dataset in combination with real images to train deep neural networks for room layout estimation and demonstrate improved performance on benchmark datasets.

Includes members:
  Member "floor sp inverse cad for floorplans by sequential room wise shortest path":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "3d manhattan room layout reconstruction from a single 360 image":
    also connected through other hyperedges:
      (none)
  Member "single image piece wise planar 3d reconstruction via associative embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning dense voxel embeddings for 3d neuron reconstruction")
  Member "horizonnet learning room layout with 1d representation and pano stretch data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition")
  Member "learning to reconstruct 3d manhattan wireframes from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning structure appearance joint embedding for indoor scene image synthesis")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "learning to adapt structured output space for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "dula net a dual projection network for estimating room layouts from a single rgb panorama":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition")

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)