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 with training wheels speeding up training with a simple controller for deep reinforcement learning"
  Abstract: Deep Reinforcement Learning (DRL) has been applied successfully to many robotic applications. However, the large number of trials needed for training is a key issue. Most of existing techniques developed to improve training efficiency (e.g. imitation) target on general tasks rather than being tailored for robot applications, which have their specific context to benefit from. We propose a novel framework, Assisted Reinforcement Learning, where a classical controller (e.g. a PID controller) is used as an alternative, switchable policy to speed up training of DRL for local planning and navigation problems. The core idea is that the simple control law allows the robot to rapidly learn sensible primitives, like driving in a straight line, instead of random exploration. As the actor network becomes more advanced, it can then take over to perform more complex actions, like obstacle avoidance. Eventually, the simple controller can be discarded entirely. We show that not only does this technique train faster, it also is less sensitive to the structure of the DRL network and consistently outperforms a standard Deep Deterministic Policy Gradient network. We demonstrate the results in both simulation and real-world experiments.

Connected through hyperedge HE-1 (induced by source paper "danger aware adaptive composition of drl agents for self navigation"):
  with members:
    - "multi advisor reinforcement learning"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-2 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "policies modulating trajectory generators"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-3 (induced by source paper "map less navigation a single drl based controller for robots with varied dimensions"):
  with members:
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning with stochastic guidance for navigation"
    - "addressing function approximation error in actor critic methods"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "robot navigation with map based deep reinforcement learning"):
  with members:
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep reinforcement learning with successor features for navigation across similar environments"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "ac teach a bayesian actor critic method for policy learning with an ensemble of suboptimal teachers"):
  with members:
    - "crossnorm normalization for off policy td reinforcement learning"
    - "towards characterizing divergence in deep q learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "off policy deep reinforcement learning without exploration"
    - "reinforcement and imitation learning for diverse visuomotor skills"

Connected through hyperedge HE-6 (induced by source paper "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"):
  with members:
    - "estimating risk and uncertainty in deep reinforcement learning"
    - "policies modulating trajectory generators"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "residual policy learning"
    - "on evaluation of embodied navigation agents"
    - "residual reinforcement learning for robot control"
    - "addressing function approximation error in actor critic methods"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "combining benefits from trajectory optimization and deep reinforcement learning"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "sim to real learning agile locomotion for quadruped robots"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "learning to walk via deep reinforcement learning"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-8 (induced by source paper "accelerating reinforcement learning with suboptimal guidance"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "continuous control with deep reinforcement learning"
    - "a reduction of imitation learning and structured prediction to no regret online 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)