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: "learning manipulation trajectories using recurrent neural networks"
  Abstract: Robots assisting disabled or elderly people in the performance of activities of daily living need to perform complex manipulation tasks which are highly dependent on the environment and preferences of the user. In addition, these environments and users are not suitable for the collection of massive amounts of training data, as the manipulated objects can be fragile, and the wheelchair-bound users might have difficulty recovering from a failed manipulation task. #R##N#In this paper, we propose an end-to-end learning mechanism for the type of complex robot arm trajectories used in manipulation tasks for assistive robots. The trajectory is learned using a recurrent neural network that can generate the trajectory in real-time based on the current situation of the end-effector, the objects in the environment and the preferences of the user. The learning data is acquired from a simulation environment where the human can demonstrate the task in a simulation closely modeling his or her own environment. Experiments using two different manipulation tasks show that the robot can learn the manipulation planning as well the ability to recover from failure.

Connected through hyperedge HE-1 (induced by source paper "auto conditioned recurrent mixture density networks for complex trajectory generation"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "scaling simulation to real transfer by learning composable robot skills"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"

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)