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: "real world multi object multi grasp detection"
  Abstract: A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise and object- wise splits, respectively. Evaluation on a multi-object dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 88.0% grasping success rates on a test set of household objects. The real-time process takes less than .25 s from image to plan.

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"
    - "visual manipulation relationship network"
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "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:
    - "visual manipulation relationship network"
    - "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)