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: "machine learning and evolutionary techniques in interplanetary trajectory design"
  Abstract: After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth–Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.

Connected through hyperedge HE-1 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "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-2 (induced by source paper "learning the optimal state feedback via supervised imitation learning"):
  with members:
    - "real time optimal control via deep neural networks study on landing problems"
    - "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-3 (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"
    - "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-4 (induced by source paper "solar sail trajectory design for multiple near earth asteroid exploration based on deep neural networks"):
  with members:
    - "multi rendezvous spacecraft trajectory optimization with beam p aco"
    - "real time optimal control via deep neural networks study on landing problems"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-5 (induced by source paper "adding neural network controllers to behavior trees without destroying performance guarantees"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "a lyapunov based approach to safe reinforcement learning"
    - "deep learning for video game playing"
    - "simulation results on selector adaptation in behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"
    - "learning of behavior trees for autonomous agents"
    - "a neural algorithm of artistic style"
    - "a framework for constrained and adaptive behavior based agents"

Connected through hyperedge HE-6 (induced by source paper "a survey on artificial intelligence trends in spacecraft guidance dynamics and control"):
  with members:
    - "deep reinforcement learning for six degree of freedom planetary powered descent and landing"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "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)