human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "multi modal attention based fusion model for semantic segmentation of rgb depth images"
  Abstract: The 3D scene understanding is mainly considered as a crucial requirement in computer vision and robotics applications. One of the high-level tasks in 3D scene understanding is semantic segmentation of RGB-Depth images. With the availability of RGB-D cameras, it is desired to improve the accuracy of the scene understanding process by exploiting the depth features along with the appearance features. As depth images are independent of illumination, they can improve the quality of semantic labeling alongside RGB images. Consideration of both common and specific features of these two modalities improves the performance of semantic segmentation. One of the main problems in RGB-Depth semantic segmentation is how to fuse or combine these two modalities to achieve more advantages of each modality while being computationally efficient. Recently, the methods that encounter deep convolutional neural networks have reached the state-of-the-art results by early, late, and middle fusion strategies. In this paper, an efficient encoder-decoder model with the attention-based fusion block is proposed to integrate mutual influences between feature maps of these two modalities. This block explicitly extracts the interdependences among concatenated feature maps of these modalities to exploit more powerful feature maps from RGB-Depth images. The extensive experimental results on three main challenging datasets of NYU-V2, SUN RGB-D, and Stanford 2D-3D-Semantic show that the proposed network outperforms the state-of-the-art models with respect to computational cost as well as model size. Experimental results also illustrate the effectiveness of the proposed lightweight attention-based fusion model in terms of accuracy.

Question: Please tell me which class the center node 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)