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: "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
  Abstract: We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the final iteration of the system and describe the emergent solution by comparing it with key robotics design aspects. Following our experience, we propose a new design aspect, precision vs. redundancy, that should be considered alongside the previously proposed design aspects of modularity vs. integration, generality vs. assumptions, computation vs. embodiment and planning vs. feedback. We present the grasping system behind Cartman, the winning robot in the 2017 Amazon Robotics Challenge. The system makes strong use of redundancy in design by implementing complimentary tools, a suction gripper and a parallel gripper. This multi-modal end-effector is combined with three grasp synthesis algorithms to accommodate the range of objects provided by Amazon during the challenge. We provide a detailed system description and an evaluation of its performance before discussing the broader nature of the system with respect to the key aspects of robotic design as initially proposed by the winners of the first Amazon Picking Challenge. To address the principal nature of our grasping system and the reason for its success, we propose an additional robotic design aspect `precision vs. redundancy'. The full design of our robotic system, including the end-effector, is open sourced and available at this http URL

Connected through hyperedge HE-1 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "resultant based incremental recovery of camera pose from pairwise matches"
    - "self supervised robot in hand object learning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-2 (induced by source paper "vision based picking system for automatic express package dispatching"):
  with members:
    - "grasp pose detection in point clouds"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "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"
    - "high precision grasp pose detection in dense clutter"
    - "yolo9000 better faster stronger"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-3 (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"
    - "semantic segmentation from limited training data"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-4 (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"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "lessons from the amazon picking challenge"

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)