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: "classification based grasp detection using spatial transformer network"
  Abstract: Robotic grasp detection task is still challenging, particularly for novel objects. With the recent advance of deep learning, there have been several works on detecting robotic grasp using neural networks. Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy. However, classification based robotic grasp detection still seems to have merits such as intermediate step observability and straightforward back propagation routine for end-to-end training. In this work, we propose a novel classification based robotic grasp detection method with multiple-stage spatial transformer networks (STN). Our proposed method was able to achieve state-of-the-art performance in accuracy with real- time computation. Additionally, unlike other regression based grasp detection methods, our proposed method allows partial observation for intermediate results such as grasp location and orientation for a number of grasp configuration candidates.

Connected through hyperedge HE-1 (induced by source paper "gq stn optimizing one shot grasp detection based on robustness classifier"):
  with members:
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "rotation ensemble module for detecting rotation invariant features"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic grasp detection using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "using synthetic data and deep networks to recognize primitive shapes for object grasping"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "efficient fully convolution neural network for generating pixel wise robotic grasps with high resolution images"):
  with members:
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "deep grasp detection and localization of grasps with deep neural networks"
    - "robotic grasp detection using deep convolutional neural networks"
    - "real time grasp detection using convolutional neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "deep learning for detecting robotic grasps"

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)