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: "fast convergence for object detection by learning how to combine error functions"
  Abstract: In this paper, we introduce an innovative method to improve the convergence speed and accuracy of object detection neural networks. Our approach, CONVERGE-FAST-AUXNET, is based on employing multiple, dependent loss metrics and weighting them optimally using an on-line trained auxiliary network. Experiments are performed in the well-known RoboCup@Work challenge environment. A fully convolutional segmentation network is trained on detecting objects' pickup points. We empirically obtain an approximate measure for the rate of success of a robotic pickup operation based on the accuracy of the object detection network. Our experiments show that adding an optimally weighted Euclidean distance loss to a network trained on the commonly used Intersection over Union (IoU) metric reduces the convergence time by 42.48%. The estimated pickup rate is improved by 39.90%. Compared to state-of-the-art task weighting methods, the improvement is 24.5% in convergence, and 15.8% on the estimated pickup rate.

Connected through hyperedge HE-1 (induced by source paper "the automated inspection of opaque liquid vaccines"):
  with members:
    - "fully convolutional one shot object segmentation for industrial robotics"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "interactive perception leveraging action in perception and perception in action"
    - "visualizing and understanding atari agents"
    - "densely connected convolutional networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "generating videos with scene dynamics"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional one shot object segmentation for industrial robotics"):
  with members:
    - "one shot segmentation in clutter"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "robotic tactile perception of object properties a review"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep metric learning with angular loss"
    - "interactive perception leveraging action in perception and perception in action"
    - "weakly supervised semantic segmentation based on web image co segmentation"

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)