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: "tracking objects using 3d object proposals"
  Abstract: 3D object proposals, quickly detected regions in a 3D scene that likely contain an object of interest, are an effective approach to improve the computational efficiency and accuracy of the object detection framework. In this work, we propose a novel online method that uses our previously developed 3D object proposals, in a RGB-D video sequence, to match and track static objects in the scene using shape matching. Our main observation is that depth images provide important information about the geometry of the scene that is often ignored in object matching techniques. Our method takes less than a second in MATLAB on the UW-RGBD scene dataset on a single thread CPU and thus, has potential to be used in low-power chips in Unmanned Aerial Vehicles (UAVs), 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"
    - "locating 3d object proposals a depth based online approach"
    - "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"
    - "locating 3d object proposals a depth based online approach"
    - "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:
    - "locating 3d object proposals a depth based online approach"
    - "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.RO(Robotics)