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: "deep grasp detection and localization of grasps with deep neural networks"
  Abstract: A deep learning architecture is proposed to predict graspable locations for robotic manipulation. We consider a more realistic situation that none or multiple objects can be in a scene. By transforming grasp configuration regression into classification problem with null hypothesis competition, the deep neural network with RGB-D image input predicts multiple grasp candidates on a single unseen object, as well as predict grasp candidates on multiple novel objects in a single shot. We perform extensive experiments with our framework on different scenarios, including no object, single object, and multi-objects. We compare with state-of-the-art approaches on Cornell dataset, and show we can achieve 96.0\% and 96.1\% accuracy on image-wise split and object-wise split, respectively.

Connected through hyperedge HE-1 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pixor real time 3d object detection from point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "bop benchmark for 6d object pose estimation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"

Connected through hyperedge HE-2 (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"
    - "classification based grasp detection using spatial transformer network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a multi task convolutional neural network for autonomous robotic grasping in object stacking scenes"):
  with members:
    - "fully convolutional grasp detection network with oriented anchor box"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic grasp detection using deep convolutional neural networks"
    - "high precision grasp pose detection in dense clutter"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-4 (induced by source paper "fully convolutional grasp detection network with oriented anchor box"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deep residual learning for image recognition"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "real time grasp detection using convolutional neural networks"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-5 (induced by source paper "a real time robotic grasp approach with oriented anchor box"):
  with members:
    - "robotic grasp detection using deep convolutional neural networks"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "real time grasp detection using convolutional neural networks"
    - "data driven grasp synthesis a survey"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-6 (induced by source paper "imitation learning for object manipulation based on position force information using bilateral control"):
  with members:
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "translating videos to natural language using deep recurrent neural networks"

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)