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: "mechanical design of a cartesian manipulator for warehouse pick and place"
  Abstract: Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have direct applications from automating warehouses to harvesting fruit in agriculture. One of the main challenges associated with these difficult robotic manipulation tasks is the motion planning and control problem for multi-DoF (Degree of Freedom) manipulators. This paper presents the design and performance evaluation of a low-cost Cartesian manipulator, Cartman who took first place in the Amazon Robotics Challenge 2017. It can perform pick and place tasks of household items in a cluttered environment. The robot is capable of linear speeds of 1 m/s and angular speeds of 1.5 rad/s, capable of sub-millimetre static accuracy and safe payload capacity of 2kg. Cartman can be produced for under 10 000 AUD. The complete design is open sourced and can be found at this http URL

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"):
  with members:
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "semantic segmentation from limited training data"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a summary of team mit s approach to the amazon picking challenge 2015"

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)