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: "locating 3d object proposals a depth based online approach"
  Abstract: 2D object proposals, quickly detected regions in an image that likely contain an object of interest, are an effective approach for improving the computational efficiency and accuracy of object detection in color images. In this paper, we propose a novel online method that generates 3D object proposals in an RGB-D video sequence. Our main observation is that depth images provide important information about the geometry of the scene. Diverging from the traditional goal of 2D object proposals to provide a high recall, we aim for precise 3D proposals. We leverage on depth information per frame and multiview scene information to obtain accurate 3D object proposals. Using efficient but robust registration enables us to combine multiple frames of a scene in near real time and generate 3D bounding boxes for potential 3D regions of interest. Using standard metrics, such as precision-recall (P-R) curves and F-measure, we show that the proposed approach is significantly more accurate than the current state-of-the-art techniques. Our online approach can be integrated into simultaneous localization and mapping-based video processing for quick 3D object localization. Our method takes less than a second in MATLAB on the UW-RGBD scene data set on a single thread CPU and, thus, has potential to be used in low-power chips in unmanned aerial vehicles, quadcopters, and drones.

Connected through hyperedge HE-1 (induced by source paper "faultnet faulty rail valves detection using deep learning and computer vision"):
  with members:
    - "dense 3d reconstruction for visual tunnel inspection using unmanned aerial vehicle"
    - "efficient gan based anomaly detection"
    - "feature less stitching of cylindrical tunnel"
    - "tracking objects using 3d object proposals"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "dense 3d reconstruction for visual tunnel inspection using unmanned aerial vehicle"):
  with members:
    - "faultnet faulty rail valves detection using deep learning and computer vision"
    - "feature less stitching of cylindrical tunnel"
    - "tracking objects using 3d object proposals"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "feature less stitching of cylindrical tunnel"):
  with members:
    - "tracking objects using 3d object proposals"
    - "calibration of depth cameras using denoised depth images"

Connected through hyperedge HE-4 (induced by source paper "tracking objects using 3d object proposals"):
  with members:
    - "what makes for effective detection proposals"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "calibration of depth cameras using denoised depth images"

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)