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: "real time optimal control via deep neural networks study on landing problems"
  Abstract: Recent research on deep learning, a set of machine learning techniques able to learn deep architectures, has shown how robotic perception and action greatly benefits from these techniques. In terms of spacecraft navigation and control system, this suggests that deep architectures may be considered now to drive all or part of the on-board decision making system. In this paper this claim is investigated in more detail training deep artificial neural networks to represent the optimal control action during a pinpoint landing, assuming perfect state information. It is found to be possible to train deep networks for this purpose and that the resulting landings, driven by the trained networks, are close to simulated optimal ones. These results allow for the design of an on-board real time optimal control system able to cope with large sets of possible initial states while still producing an optimal response.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "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"
    - "plato policy learning using adaptive trajectory optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "learning the optimal state feedback via supervised imitation learning"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (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"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "control of a quadrotor with reinforcement learning"
    - "trajectory generation for quadrotor based systems using numerical optimal control"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-5 (induced by source paper "machine learning and evolutionary techniques in interplanetary trajectory design"):
  with members:
    - "multi rendezvous spacecraft trajectory optimization with beam p aco"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "mga trajectory planning with an aco inspired algorithm"

Connected through hyperedge HE-6 (induced by source paper "interplanetary transfers via deep representations of the optimal policy and or of the value function"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "solar sail trajectory design for multiple near earth asteroid exploration based on deep neural networks"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "multi rendezvous spacecraft trajectory optimization with beam p aco"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-8 (induced by source paper "fast evaluation of low thrust transfers via deep neural networks"):
  with members:
    - "an overview of gradient descent optimization algorithms"

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)