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: "lessons from the amazon picking challenge"
  Abstract: This paper summarizes lessons learned from the first Amazon Picking Challenge in which 26 international teams designed robotic systems that competed to retrieve items from warehouse shelves. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned.

Connected through hyperedge HE-1 (induced by source paper "development of a robotic system for automated decaking of 3d printed parts"):
  with members:
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "team delft s robot winner of the amazon picking challenge 2016"
    - "analysis and observations from the first amazon picking challenge"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "homebreweddb rgb d dataset for 6d pose estimation of 3d objects"):
  with members:
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "bop benchmark for 6d object pose estimation"
    - "densepose dense human pose estimation in the wild"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "imitation learning for object manipulation based on position force information using bilateral control"):
  with members:
    - "deep grasp detection and localization of grasps with deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "translating videos to natural language using deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "cut paste and learn surprisingly easy synthesis for instance detection"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "a dataset for developing and benchmarking active vision"
    - "learning from synthetic humans"
    - "synthesizing training data for object detection in indoor scenes"
    - "how useful is photo realistic rendering for visual learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "cartman the low cost cartesian manipulator that won the amazon robotics challenge"):
  with members:
    - "autonomous sweet pepper harvesting for protected cropping systems"
    - "mechanical design of a cartesian manipulator for warehouse pick and place"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "semantic segmentation from limited training data"
    - "analysis and observations from the first amazon picking challenge"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-6 (induced by source paper "integrating asymptotically optimal path planning with local optimization"):
  with members:
    - "interleaving optimization with sampling based motion planning ios mp combining local optimization with global exploration"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "bit batch informed trees for optimal sampling based planning via dynamic programming on implicit random geometric graphs"
    - "asymptotically near optimal rrt for fast high quality motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "mechanical design of a cartesian manipulator for warehouse pick and place"):
  with members:
    - "autonomous sweet pepper harvesting for protected cropping systems"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"

Connected through hyperedge HE-8 (induced by source paper "finding shorter paths for robot arms using their redundancy"):
  with members:
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"

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)