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: "multi objective convolutional neural networks for robot localisation and 3d position estimation in 2d camera images"
  Abstract: The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed setups, which have all the sensors fixed and calibrated in relation to the rest of the system. However, it becomes a limiting factor when the system needs to be reconfigured or moved. We present a deep learning approach, which aims to solve this issue. Our method learns to identify and precisely localise the robot in 2D camera images, so having a fixed setup is no longer a requirement and a camera can be moved. In addition, our approach identifies the robot type and estimates the 3D position of the robot base in the camera image as well as 3D positions of each of the robot joints. Learning is done by using a multiobjective convolutional neural network with four previously mentioned objectives simultaneously using a combined loss function. The multi-objective approach makes the system more flexible and efficient by reusing some of the same features and diversifying for each objective in lower layers. A fully trained system shows promising results in providing an accurate mask of where the robot is located and an estimate of its base and joint positions in 3D. We compare the results to our previous approach of using cascaded convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "3d robot pose estimation from 2d images"):
  with members:
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "transfer learning for unseen robot detection and joint estimation on a multi objective convolutional neural network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "towards accurate multi person pose estimation in the wild"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"

Connected through hyperedge HE-2 (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"
    - "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"
    - "automatic calibration of a robot manipulator and multi 3d camera system"
    - "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-3 (induced by source paper "transfer learning for unseen robot detection and joint estimation on a multi objective convolutional neural network"):
  with members:
    - "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"
    - "automatic calibration of a robot manipulator and multi 3d camera system"
    - "transfer learning from deep features for remote sensing and poverty mapping"

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)