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 "fully convolutional one shot object segmentation for industrial robotics"):
  Abstract: The ability to identify and localize new objects robustly and effectively is vital for robotic grasping and manipulation in warehouses or smart factories. Deep convolutional neural networks (DCNNs) have achieved the state-of-the-art performance on established image datasets for object detection and segmentation. However, applying DCNNs in dynamic industrial scenarios, e.g., warehouses and autonomous production, remains a challenging problem. DCNNs quickly become ineffective when tasked with detecting objects that they have not been trained on. Given that re-training using the latest data is time consuming, DCNNs cannot meet the requirement of the Factory of the Future (FoF) regarding rapid development and production cycles. To address this problem, we propose a novel one-shot object segmentation framework, using a fully convolutional Siamese network architecture, to detect previously unknown objects based on a single prototype image. We turn to multi-task learning to reduce training time and improve classification accuracy. Furthermore, we introduce a novel approach to automatically cluster the learnt feature space representation in a weakly supervised manner. We test the proposed framework on the RoboCup@Work dataset, simulating requirements for the FoF. Results show that the trained network on average identifies 73% of previously unseen objects correctly from a single example image. Correctly identified objects are estimated to have a 87.53% successful pick-up rate. Finally, multi-task learning lowers the convergence time by up to 33%, and increases accuracy by 2.99%.

Includes members:
  Member "fast convergence for object detection by learning how to combine error functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the automated inspection of opaque liquid vaccines")
  Member "one shot segmentation in clutter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot object detection with attention rpn and multi relation detector")
  Member "repmet representative based metric learning for classification and one shot object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "computer vision for autonomous vehicles problems datasets and state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "robotic tactile perception of object properties a review":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "t less an rgb d dataset for 6d pose estimation of texture less objects":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "deep metric learning with angular loss":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "interactive perception leveraging action in perception and perception in action":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action for better prediction")

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.RO(Robotics)