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: "rgb d slam in dynamic environments using points correlations"
  Abstract: This paper proposed a novel RGB-D SLAM method for dynamic environments. It follows traditional feature-based SLAM methods and utilizes a feature groups segmentation method to resist the disturbance caused by the dynamic objects using points correlations. The correlations between map points represented with a sparse graph are created by Delaunay triangulation. After removing non-consistency connections, the dynamic objects are separated from static background. The features only in the static map are used for motion estimation and bundle adjustment which improves the accuracy and robustness of SLAM in dynamic environments. The effectiveness of the proposed SLAM are evaluated using TUM RGB-D benchmark. The experiments demonstrate that the dynamic features are successfully removed and the system work perfectly in both low and high dynamic environments. The comparisons between proposed method and state-of-the-art visual systems clearly show that the comparable accurate results are achieved in low dynamic environments and the performance is improved significantly in high dynamic environments.

Connected through hyperedge HE-1 (induced by source paper "triangulation learning network from monocular to stereo 3d object detection"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"

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)