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: "solving the robot world hand eye s calibration problem with iterative methods"
  Abstract: Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as $${\mathbf {AX}}={\mathbf {ZB}}$$AX=ZB, where $${\mathbf {X}}$$X and $${\mathbf {Z}}$$Z are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and simulated datasets and compare to seven other state-of-the-art methods. Our collection of methods returns greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple-eye calibration, and results are shown with two and three cameras mounted on the same robot.

Connected through hyperedge HE-1 (induced by source paper "calibration of asynchronous camera networks for object reconstruction tasks"):
  with members:
    - "tree morphology for phenotyping from semantics based mapping in orchard environments"
    - "geometric loss functions for camera pose regression with deep learning"
    - "dpc net deep pose correction for visual localization"
    - "a robotic vision system to measure tree traits"
    - "relative camera pose estimation using convolutional neural networks"
    - "panoptic studio a massively multiview system for social interaction capture"

Connected through hyperedge HE-2 (induced by source paper "calibration of asynchronous camera networks calico"):
  with members:
    - "datasheets for datasets"
    - "geometric loss functions for camera pose regression with deep learning"
    - "dpc net deep pose correction for visual localization"
    - "relative camera pose estimation using convolutional neural networks"
    - "panoptic studio a massively multiview system for social interaction capture"

Connected through hyperedge HE-3 (induced by source paper "a robotic vision system to measure tree traits"):
  with members:
    - "fast and robust curve skeletonization for real world elongated objects"

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)