human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey on artificial intelligence trends in spacecraft guidance dynamics and control"):
  Abstract: The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.

Includes members:
  Member "machine learning and evolutionary techniques in interplanetary trajectory design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning a family of optimal state feedback controllers")
  Member "deep reinforcement learning for six degree of freedom planetary powered descent and landing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive guidance and integrated navigation with reinforcement meta learning")
  Member "on the stability analysis of deep neural network representations of an optimal state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "multi rendezvous spacecraft trajectory optimization with beam p aco":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning and evolutionary techniques in interplanetary trajectory design")
  Member "real time optimal control via deep neural networks study on landing problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "π nets deep polynomial neural networks")
  Member "mga trajectory planning with an aco inspired algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "machine learning and evolutionary techniques in interplanetary trajectory design")
  Member "an inflationary differential evolution algorithm for space trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximated computation of belief functions for robust design optimization")

Question: Please tell me which class the source paper 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)