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: "end to end analysis and design of a drone flight controller"
  Abstract: Timing guarantees are crucial to cyber-physical applications that must bound the end-to-end delay between sensing, processing and actuation. For example, in a flight controller for a multirotor drone, the data from a gyro or inertial sensor must be gathered and processed to determine the attitude of the aircraft. Sensor data fusion is followed by control decisions that adjust the flight of a drone by altering motor speeds. If the processing pipeline between sensor input and actuation is not bounded, the drone will lose control and possibly fail to maintain flight. #R##N#Motivated by the implementation of a multithreaded drone flight controller on the Quest RTOS, we develop a composable pipe model based on the system's task, scheduling and communication abstractions. This pipe model is used to analyze two semantics of end-to-end time: reaction time and freshness time. We also argue that end-to-end timing properties should be factored in at the early stage of application design. Thus, we provide a mathematical framework to derive feasible task periods that satisfy both a given set of end-to-end timing constraints and the schedulability requirement. We demonstrate the applicability of our design approach by using it to port the Cleanflight flight controller firmware to Quest on the Intel Aero board. Experiments show that Cleanflight ported to Quest is able to achieve end-to-end latencies within the predicted time bounds derived by analysis.

Connected through hyperedge HE-1 (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"
    - "reinforcement learning for uav attitude control"
    - "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"

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.SY(Systems and Control)