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: "imitation learning for object manipulation based on position force information using bilateral control"
  Abstract: This study proposes an imitation learning method based on force and position information. Force information is required for precise object manipulation but is difficult to obtain because the acting and reaction forces cannnot be separated. To separate the forces, we proposed to introduce bilateral control, in which the acting and reaction forces are divided using two robots. In the proposed method, two models of neural networks learn a task; to draw a line along a ruler. We verify the possibility that force information is essential to imitate the human skill of object manipulation.

Connected through hyperedge HE-1 (induced by source paper "imitation learning for human robot cooperation using bilateral control"):
  with members:
    - "time series motion generation considering long short term motion"
    - "improving dual arm assembly by master slave compliance"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "adam a method for stochastic optimization"
    - "real time grasp detection using convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "time series motion generation considering long short term motion"):
  with members:
    - "baxter s homunculus virtual reality spaces for teleoperation in manufacturing"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "sequence level training with recurrent neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "translating videos to natural language using deep recurrent neural networks"

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)