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: "on the stability analysis of deep neural network representations of an optimal state feedback"
  Abstract: Recent work have shown how the optimal state-feedback, obtained as the solution to the Hamilton-Jacobi-Bellman equations, can be approximated for several nonlinear, deterministic systems by deep neural networks. When imitation (supervised) learning is used to train the neural network on optimal state-action pairs, for instance as derived by applying Pontryagin's theory of optimal processes, the resulting model is referred here as the guidance and control network. In this work, we analyze the stability of nonlinear and deterministic systems controlled by such networks. We then propose a method utilising differential algebraic techniques and high-order Taylor maps to gain information on the stability of the neurocontrolled state trajectories. We exemplify the proposed methods in the case of the two-dimensional dynamics of a quadcopter controlled to reach the origin and we study how different architectures of the guidance and control network affect the stability of the target equilibrium point and the stability margins to time delay. Moreover, we show how to study the robustness to initial conditions of a nominal trajectory, using a Taylor representation of the neurocontrolled neighbouring trajectories.

Connected through hyperedge HE-1 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "interplanetary transfers via deep representations of the optimal policy and or of the value function"
    - "a survey on artificial intelligence trends in spacecraft guidance dynamics and control"
    - "real time optimal control via deep neural networks study on landing problems"

Connected through hyperedge HE-2 (induced by source paper "aggressive online control of a quadrotor via deep network representations of optimality principles"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high speed trajectories"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "control of a quadrotor with reinforcement learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "trajectory generation for quadrotor based systems using numerical optimal control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-3 (induced by source paper "a survey on artificial intelligence trends in spacecraft guidance dynamics and control"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "deep reinforcement learning for six degree of freedom planetary powered descent and landing"
    - "multi rendezvous spacecraft trajectory optimization with beam p aco"
    - "real time optimal control via deep neural networks study on landing problems"
    - "deep learning in neural networks"
    - "mga trajectory planning with an aco inspired algorithm"
    - "an inflationary differential evolution algorithm for space trajectory optimization"

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.NE(Neural and Evolutionary Computing)