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: "multi rendezvous spacecraft trajectory optimization with beam p aco"
  Abstract: The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.

Connected through hyperedge HE-1 (induced by source paper "machine learning and evolutionary techniques in interplanetary trajectory design"):
  with members:
    - "real time optimal control via deep neural networks study on landing problems"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "mga trajectory planning with an aco inspired algorithm"

Connected through hyperedge HE-2 (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"
    - "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-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"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "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)