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 "what s in my room object recognition on indoor panoramic images"):
  Abstract: In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still remains a challenging problem that has not been deeply investigated. This paper provides an object recognition system that performs object detection and semantic segmentation tasks by using a deep learning model adapted to match the nature of equirectangular images. From these results, instance segmentation masks are recovered, refined and transformed into 3D bounding boxes that are placed into the 3D model of the room. Quantitative and qualitative results support that our method outperforms the state of the art by a large margin and show a complete understanding of the main objects in indoor scenes.

Includes members:
  Member "corners for layout end to end layout recovery from 360 images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "360 o surface regression with a hyper sphere loss")
  Member "horizonnet learning room layout with 1d representation and pano stretch data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the direction aware learnable additive kernels and the adversarial network for deep floor plan recognition")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "layouts from panoramic images with geometry and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "corners for layout end to end layout recovery from 360 images")
  Member "blitznet a real time deep network for scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "joint 2d 3d semantic data for indoor scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "semantic scene completion from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)