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: "sim to multi real transfer of low level robust control policies to multiple quadrotors"
  Abstract: Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are low-level, i.e., we map the rotorcrafts' state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. We show how different training methodologies (change of the cost function, modeling of noise, use of domain randomization) might affect flight performance. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing low-level quadrotor controller (without the use of a stabilizing PD controller) that is shown to generalize to multiple quadrotors.

Connected through hyperedge HE-1 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "fly safe aerial swarm robotics using force field particle swarm optimisation"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"

Connected through hyperedge HE-2 (induced by source paper "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving"):
  with members:
    - "a fleet of miniature cars for experiments in cooperative driving"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "flight controller synthesis via deep reinforcement learning"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "neuroflight next generation flight control firmware"
    - "learning dexterous in hand manipulation"
    - "reinforcement learning for uav attitude control"
    - "end to end analysis and design of a drone flight controller"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "deep reinforcement learning that matters"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "neural swarm decentralized close proximity multirotor controlusing learned interactions"):
  with members:
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "residual reinforcement learning for robot control"
    - "deep sets"
    - "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"
    - "smooth imitation learning for online sequence prediction"

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)