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 quadrotor dynamics using neural network for flight control"
  Abstract: Traditional learning approaches proposed for controlling quadrotors or helicopters have focused on improving performance for specific trajectories by iteratively improving upon a nominal controller, for example learning from demonstrations, iterative learning, and reinforcement learning. In these schemes, however, it is not clear how the information gathered from the training trajectories can be used to synthesize controllers for more general trajectories. Recently, the efficacy of deep learning in inferring helicopter dynamics has been shown. Motivated by the generalization capability of deep learning, this paper investigates whether a neural network based dynamics model can be employed to synthesize control for trajectories different than those used for training. To test this, we learn a quadrotor dynamics model using only translational and only rotational training trajectories, each of which can be controlled independently, and then use it to simultaneously control the yaw and position of a quadrotor, which is non-trivial because of nonlinear couplings between the two motions. We validate our approach in experiments on a quadrotor testbed.

Connected through hyperedge HE-1 (induced by source paper "validation of image based neural network controllers through adaptive stress testing"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "reachability analysis and safety verification for neural network control systems"
    - "efficient formal safety analysis of neural networks"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards proving the adversarial robustness of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "a new simulation metric to determine safe environments and controllers for systems with unknown dynamics"):
  with members:
    - "soter programming safe robotics system using runtime assurance"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "goal driven dynamics learning via bayesian optimization"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-3 (induced by source paper "low level control of a quadrotor with deep model based reinforcement learning"):
  with members:
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to adapt meta learning for model based control"
    - "control of a quadrotor with reinforcement learning"
    - "swish a self gated activation function"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "online deep learning for improved trajectory tracking of unmanned aerial vehicles using expert knowledge"):
  with members:
    - "gapflyt active vision based minimalist structure less gap detection for quadrotor flight"
    - "an inversion based learning approach for improving impromptu trajectory tracking of robots with non minimum phase dynamics"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-5 (induced by source paper "learning to adapt meta learning for model based control"):
  with members:
    - "model ensemble trust region policy optimization"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to learn meta critic networks for sample efficient learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "rapid adaptation with conditionally shifted neurons"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-6 (induced by source paper "neural lander stable drone landing control using learned dynamics"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "exploring generalization in deep learning"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "safe controller optimization for quadrotors with gaussian processes"

Connected through hyperedge HE-7 (induced by source paper "a classification based approach for approximate reachability"):
  with members:
    - "reverse curriculum generation for reinforcement learning"
    - "neural network with unbounded activation functions is universal approximator"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "using neural networks to compute approximate and guaranteed feasible hamilton jacobi bellman pde solutions"
    - "plato policy learning using adaptive trajectory optimization"
    - "synthesis of separable controlled invariant sets for modular local control design"

Connected through hyperedge HE-8 (induced by source paper "context specific validation of data driven models"):
  with members:
    - "exponential regret bounds for gaussian process bandits with deterministic observations"
    - "x armed bandits"

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)