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 "object driven multi layer scene decomposition from a single image"):
  Abstract: We present a method that tackles the challenge of predicting color and depth behind the visible content of an image. Our approach aims at building up a Layered Depth Image (LDI) from a single RGB input, which is an efficient representation that arranges the scene in layers, including originally occluded regions. Unlike previous work, we enable an adaptive scheme for the number of layers and incorporate semantic encoding for better hallucination of partly occluded objects. Additionally, our approach is object-driven, which especially boosts the accuracy for the occluded intermediate objects. The framework consists of two steps. First, we individually complete each object in terms of color and depth, while estimating the scene layout. Second, we rebuild the scene based on the regressed layers and enforce the recomposed image to resemble the structure of the original input. The learned representation enables various applications, such as 3D photography and diminished reality, all from a single RGB image.

Includes members:
  Member "peeking behind objects layered depth prediction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d scene reconstruction with multi layer depth and epipolar transformers")
  Member "layoutnet reconstructing the 3d room layout from a single rgb image":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scan2plan efficient floorplan generation from 3d scans of indoor scenes")
  Member "deep depth completion of a single rgb d image":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "planercnn 3d plane detection and reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from planes to corners multi purpose primitive detection in unorganized 3d point clouds")
  Member "layer structured 3d scene inference via view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image")
  Member "stereo magnification learning view synthesis using multiplane images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing the world in a bag of chips")
  Member "adversarial semantic scene completion from a single depth image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "depth based semantic scene completion with position importance aware loss")
  Member "segan segmenting and generating the invisible":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the impact of hole geometry on relative robustness of in painting networks an empirical study")

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)