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: "a review of control algorithms for autonomous quadrotors"
  Abstract: The quadrotor unmanned aerial vehicle is a great platform for control systems research as its nonlinear nature and under-actuated configuration make it ideal to synthesize and analyze control algorithms. After a brief explanation of the system, several algorithms have been analyzed including their advantages and disadvantages: PID, Linear Quadratic Regulator (LQR), Sliding mode, Backstepping, Feedback linearization, Adaptive, Robust, Optimal, L1, H∞, Fuzzy logic and Artificial neutral networks. The conclusion of this work is a proposal of hybrid systems to be considered as they combine advantages from more than one control philosophy.

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"
    - "end to end analysis and design of a drone flight controller"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-2 (induced by source paper "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"):
  with members:
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "oil observational imitation learning"
    - "uplink coverage performance of an underlay drone cell for temporary events"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "ultra low power deep learning powered autonomous nano drones"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "how hard is it to cross the room training recurrent neural networks to steer a uav"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"

Connected through hyperedge HE-3 (induced by source paper "nonlinear pid controller design for a 6 dof uav quadrotor system"):
  with members:
    - "on the design of a novel finite time nonlinear extended state observer for class of nonlinear systems with mismatch disturbances and uncertainties"

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)