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 "gq stn optimizing one shot grasp detection based on robustness classifier"):
  Abstract: Grasping is a fundamental robotic task needed for the deployment of household robots or furthering warehouse automation. However, few approaches are able to perform grasp detection in real time (frame rate). To this effect, we present Grasp Quality Spatial Transformer Network (GQ-STN), a one-shot grasp detection network. Being based on the Spatial Transformer Network (STN), it produces not only a grasp configuration, but also directly outputs a depth image centered at this configuration. By connecting our architecture to an externally-trained grasp robustness evaluation network, we can train efficiently to satisfy a robustness metric via the backpropagation of the gradient emanating from the evaluation network. This removes the difficulty of training detection networks on sparsely annotated databases, a common issue in grasping. We further propose to use this robustness classifier to compare approaches, being more reliable than the traditional rectangle metric. Our GQ-STN is able to detect robust grasps on the depth images of the Dex-Net 2.0 dataset with 92.4 % accuracy in a single pass of the network. We finally demonstrate in a physical benchmark that our method can propose robust grasps more often than previous sampling-based methods, while being more than 60 times faster.

Includes members:
  Member "dealing with ambiguity in robotic grasping via multiple predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "roi based robotic grasp detection for object overlapping scenes")
  Member "rotation ensemble module for detecting rotation invariant features":
    also connected through other hyperedges:
      (none)
  Member "fully convolutional grasp detection network with oriented anchor box":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "roi based robotic grasp detection in object overlapping scenes using convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graspnet a large scale clustered and densely annotated datase for object grasping")
  Member "classification based grasp detection using spatial transformer network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using synthetic data and deep networks to recognize primitive shapes for object grasping")
  Member "learning object localization and 6d pose estimation from simulation and weakly labeled real images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "jacquard a large scale dataset for robotic grasp detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")
  Member "using simulation and domain adaptation to improve efficiency of deep robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)