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: "recurrent convolutional fusion for rgb d object recognition"
  Abstract: Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision. The introduction of RGB-D cameras has paved the way for a significant leap forward in this direction thanks to the rich information provided by these sensors. However, the machine vision community still lacks an effective method to synergically use the RGB and depth data to improve object recognition. In order to take a step in this direction, we introduce a novel end-to-end architecture for RGB-D object recognition called recurrent convolutional fusion (RCFusion). Our method generates compact and highly discriminative multi-modal features by combining complementary RGB and depth information representing different levels of abstraction. Extensive experiments on two popular datasets, RGB-D Object Dataset and JHUIT-50, show that RCFusion significantly outperforms state-of-the-art approaches in both the object categorization and instance recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "6d pose estimation with correlation fusion"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "segmentation driven 6d object pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "deep learning vs traditional computer vision"):
  with members:
    - "hybrid bayesian eigenobjects combining linear subspace and deep network methods for 3d robot vision"
    - "hybrid scene compression for visual localization"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "significance of softmax based features in comparison to distance metric learning based features"

Connected through hyperedge HE-3 (induced by source paper "a recurrent multi scale approach to rbg d object recognition"):
  with members:
    - "mask r cnn"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "a deep representation for depth images from synthetic data"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "multimodal deep learning for robust rgb d object recognition"

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)