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: "reinforced imitation sample efficient deep reinforcement learning for map less navigation by leveraging prior demonstrations"
  Abstract: This work presents a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained using a combination of expert demonstrations, imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer from a large sample complexity and the distribution mismatch problem, respectively, we show that pre-training the navigation model using expert demonstrations can reduce the training time to reach at least the same level of performance compared to plain RL by a factor of 5. We present a thorough evaluation of different combinations of expert demonstrations and RL, both in simulation and on a real robotic platform. Our results show that the final model outperforms both standalone approaches in the amount of successful navigation tasks. The learned navigation policy is also able to generalize to unseen and real-world environments.

Connected through hyperedge HE-1 (induced by source paper "harnessing reinforcement learning for neural motion planning"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "deep learning using rectified linear units relu"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "motion planning networks"
    - "deep sequential models for sampling based planning"

Connected through hyperedge HE-2 (induced by source paper "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"

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)