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: "reinforcement learning for uav attitude control"
  Abstract: Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e.g. way-point navigation. Autopilot systems for UAVs are predominately implemented using Proportional, Integral Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However more sophisticated control is required to operate in unpredictable, and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL) which has had success in other applications such as robotics. However previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with the state-of-the-art RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate these unknowns we first developed an open-source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then use our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.

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"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "communication efficient massive uav online path control federated learning meets mean field game theory"):
  with members:
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "remote uav online path planning via neural network based opportunistic control"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "autonomous uav navigation using reinforcement learning"
    - "virtualized control over fog interplay between reliability and latency"
    - "energy efficient uav communication with trajectory optimization"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-3 (induced by source paper "mid flight propeller failure detection and control of propeller deficient quadcopter using reinforcement learning"):
  with members:
    - "control of a quadrotor with reinforcement learning"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (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"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "learning dexterous in hand manipulation"
    - "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-6 (induced by source paper "augmented random search for quadcopter control an alternative to reinforcement learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning data augmentation policies using augmented random search"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "guiding the search in continuous state action spaces by learning an action sampling distribution from off target samples"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "towards generalization and simplicity in continuous control"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-7 (induced by source paper "a meta mdp approach to exploration for lifelong reinforcement learning"):
  with members:
    - "multi advisor reinforcement learning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "hybrid reward architecture for reinforcement learning"
    - "count based exploration in feature space for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a laplacian framework for option discovery in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning to seek autonomous source seeking on a nano drone microcontroller with deep reinforcement learning"):
  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"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "soft actor critic algorithms and applications"
    - "ultra low power deep learning powered autonomous nano drones"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)