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 the optimal state feedback via supervised imitation learning"
  Abstract: Imitation learning is a control design paradigm that seeks to learn a control policy reproducing demonstrations from expert agents. By substituting expert demonstrations for optimal behaviours, the same paradigm leads to the design of control policies closely approximating the optimal state-feedback. This approach requires training a machine learning algorithm (in our case deep neural networks) directly on state-control pairs originating from optimal trajectories. We have shown in previous work that, when restricted to low-dimensional state and control spaces, this approach is very successful in several deterministic, non-linear problems in continuous-time. In this work, we refine our previous studies using as a test case a simple quadcopter model with quadratic and time-optimal objective functions. We describe in detail the best learning pipeline we have developed, that is able to approximate via deep neural networks the state-feedback map to a very high accuracy. We introduce the use of the softplus activation function in the hidden units of neural networks showing that it results in a smoother control profile whilst retaining the benefits of rectifiers. We show how to evaluate the optimality of the trained state-feedback, and find that already with two layers the objective function reached and its optimal value differ by less than one percent. We later consider also an additional metric linked to the system asymptotic behaviour - time taken to converge to the policy's fixed point. With respect to these metrics, we show that improvements in the mean absolute error do not necessarily correspond to better policies.

Connected through hyperedge HE-1 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks"):
  with members:
    - "interplanetary transfers via deep representations of the optimal policy and or of the value function"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "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 "learning a family of optimal state feedback controllers"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "plato policy learning using adaptive trajectory optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "aggressive online control of a quadrotor via deep network representations of optimality principles"):
  with members:
    - "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"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "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-4 (induced by source paper "interplanetary transfers via deep representations of the optimal policy and or of the value function"):
  with members:
    - "learning a family of optimal state feedback controllers"
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "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-5 (induced by source paper "on the stability analysis of deep neural network representations of an optimal state feedback"):
  with members:
    - "generative adversarial imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "exploring deep and recurrent architectures for optimal control"

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.LG(Machine Learning)