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: "automatic calibration of a robot manipulator and multi 3d camera system"
  Abstract: With 3D sensing becoming cheaper, environment-aware and visually-guided robot arms capable of safely working in collaboration with humans will become common. However, a reliable calibration is needed, both for camera internal calibration, as well as Eye-to-Hand calibration, to make sure the whole system functions correctly. We present a framework, using a novel combination of well proven methods, allowing a quick automatic calibration for the integration of systems consisting of the robot and a varying number of 3D cameras by using a standard checkerboard calibration grid. Our approach allows a quick camera-to-robot recalibration after any changes to the setup, for example when cameras or robot have been repositioned. Modular design of the system ensures flexibility regarding a number of sensors used as well as different hardware choices. The framework has been proven to work by practical experiments to analyze the quality of the calibration versus the number of positions of the checkerboard used for each of the calibration procedures.

Connected through hyperedge HE-1 (induced by source paper "two stage transfer learning for heterogeneous robot detection and 3d joint position estimation in a 2d camera image using cnn"):
  with members:
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "multi objective convolutional neural networks for robot localisation and 3d position estimation in 2d camera images"
    - "transfer learning for unseen robot detection and joint estimation on a multi objective convolutional neural network"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "multi 3d camera mapping for predictive and reflexive robot manipulator trajectory estimation"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-2 (induced by source paper "multi objective convolutional neural networks for robot localisation and 3d position estimation in 2d camera images"):
  with members:
    - "robot localisation and 3d position estimation using a free moving camera and cascaded convolutional neural networks"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "facial landmark detection with tweaked convolutional neural networks"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for unseen robot detection and joint estimation on a multi objective convolutional neural network"):
  with members:
    - "multi objective convolutional neural networks for robot localisation and 3d position estimation in 2d camera images"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "multi 3d camera mapping for predictive and reflexive robot manipulator trajectory estimation"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-4 (induced by source paper "robot localisation and 3d position estimation using a free moving camera and cascaded convolutional neural networks"):
  with members:
    - "multi 3d camera mapping for predictive and reflexive robot manipulator trajectory estimation"

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)