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 "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.

Includes members:
  Member "3d object proposals using stereo imagery for accurate object class detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "what makes for effective detection proposals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deeply activated salient region for instance search")
  Member "deep sliding shapes for amodal 3d object detection in rgb d images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "monocular slam supported object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "alpha n shortest path finder automated delivery robot with obstacle detection and avoiding system")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deepbox learning objectness with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scale matters temporal scale aggregation network for precise action localization in untrimmed videos")
  Member "learning rich features from rgb d images for object detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep geometry learning from a representation perspective")

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)