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 "3d gated recurrent fusion for semantic scene completion"):
  Abstract: This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of high relevance for shape completion. Using both RGB and depth images can further boost the accuracy of SSC over employing one modality in isolation. We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network. Extensive experiments on two benchmark datasets demonstrate the superior performance and the effectiveness of the proposed GRFNet for data fusion in SSC. Code will be made available.

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 "depth based semantic scene completion with position importance aware loss")
  Member "adaptive fusion for rgb d salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection")
  Member "efficient semantic scene completion network with spatial group convolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "depth based semantic scene completion with position importance aware loss")
  Member "scalable place recognition under appearance change for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved visual localization via graph smoothing")
  Member "two stream 3d semantic scene completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "depth based semantic scene completion with position importance aware loss")
  Member "unsupervised rgbd video object segmentation using gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "illumination invariant foreground object segmentation using foregans")
  Member "rgb d based action recognition with light weight 3d convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn")
  Member "high resolution shape completion using deep neural networks for global structure and local geometry inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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)