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 "cascaded context pyramid for full resolution 3d semantic scene completion"):
  Abstract: Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. It helps intelligent devices to understand and interact with the surrounding scenes. Due to the high-memory requirement, current methods only produce low-resolution completion predictions, and generally lose the object details. Furthermore, they also ignore the multi-scale spatial contexts, which play a vital role for the 3D inference. To address these issues, in this work we propose a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image. The proposed CCPNet improves the labeling coherence with a cascaded context pyramid. Meanwhile, based on the low-level features, it progressively restores the fine-structures of objects with Guided Residual Refinement (GRR) modules. Our proposed framework has three outstanding advantages: (1) it explicitly models the 3D spatial context for performance improvement; (2) full-resolution 3D volumes are produced with structure-preserving details; (3) light-weight models with low-memory requirements are captured with a good extensibility. Extensive experiments demonstrate that in spite of taking a single-view depth map, our proposed framework can generate high-quality SSC results, and outperforms state-of-the-art approaches on both the synthetic SUNCG and real NYU datasets.

Includes members:
  Member "rgbd based dimensional decomposition residual network for 3d semantic scene completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d gated recurrent fusion for semantic scene completion")
  Member "efficient semantic scene completion network with spatial group convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d gated recurrent fusion for semantic scene completion")
  Member "semantic scene completion combining colour and depth preliminary experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rgbd based dimensional decomposition residual network for 3d semantic scene completion")
  Member "agile amulet real time salient object detection with contextual attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "salient object detection with lossless feature reflection and weighted structural loss")
  Member "depth aware cnn for rgb d segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scene completenesss aware lidar depth completion for driving scenario")
  Member "two stream 3d semantic scene completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d gated recurrent fusion for semantic scene completion")
  Member "view volume network for 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 "semantic labeling in very high resolution images via a self cascaded convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")

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)