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: "visual manipulation relationship network"
  Abstract: Grasping is one of the most significant manip- ulation in everyday life, which can be influenced a lot by grasping order when there are several objects in the scene. Therefore, the manipulation relationships are needed to help robot better grasp and manipulate objects. This paper presents a new convolutional neural network architecture called Visual Manipulation Relationship Network (VMRN), which is used to help robot detect targets and predict the manipulation relationships in real time. To implement end-to-end training and meet real-time requirements in robot tasks, we propose the Object Pairing Pooling Layer (OP2L), which can help to predict all manipulation relationships in one forward process. To train VMRN, we collect a dataset named Visual Manipulation Rela- tionship Dataset (VMRD) consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image, which is labeled by the manipulation relationship tree. The experiment results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the real-time requirements in robot tasks.

Connected through hyperedge HE-1 (induced by source paper "roi based robotic grasp detection for object overlapping scenes"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "real world multi object multi grasp detection"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"):
  with members:
    - "real world multi object multi grasp detection"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "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"

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)