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: "teaching robots to do object assembly using multi modal 3d vision"
  Abstract: The motivation of this paper is to develop a smart system using multi-modal vision for next-generation mechanical assembly. It includes two phases where in the first phase human beings teach the assembly structure to a robot and in the second phase the robot finds objects and grasps and assembles them using AI planning. The crucial part of the system is the precision of 3D visual detection and the paper presents multi-modal approaches to meet the requirements: AR markers are used in the teaching phase since human beings can actively control the process. Point cloud matching and geometric constraints are used in the robot execution phase to avoid unexpected noises. Experiments are performed to examine the precision and correctness of the approaches. The study is practical: The developed approaches are integrated with graph model-based motion planning, implemented on an industrial robots and applicable to real-world scenarios.

Connected through hyperedge HE-1 (induced by source paper "designing a mechanical tool for robots with two finger parallel grippers"):
  with members:
    - "a double jaw hand designed for multi object assembly"
    - "a hand combining two simple grippers to pick up and arrange objects for assembly"
    - "arm manipulation planning of tethered tools with the help of a tool balancer"
    - "closed chain manipulation of large objects by multi arm robotic systems"
    - "a certified complete bimanual manipulation planner"
    - "manipulation planning under changing external forces"

Connected through hyperedge HE-2 (induced by source paper "assembly planning by subassembly decomposition using blocking reduction"):
  with members:
    - "analyzing the utility of a support pin in sequential robotic manipulation"

Connected through hyperedge HE-3 (induced by source paper "a double jaw hand designed for multi object assembly"):
  with members:
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "a compliant underactuated hand for robust manipulation"

Connected through hyperedge HE-4 (induced by source paper "dorapicker an autonomous picking system for general objects"):
  with members:
    - "lessons from the amazon picking challenge"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"

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)