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: "improved regularity model based eda for many objective optimization"
  Abstract: The performance of multiobjective evolutionary algorithms deteriorates appreciably in solving many-objective optimization problems (MaOPs) which encompass more than three objectives. One of the known rationales is the loss of selection pressure which leads to the selected parents not generating promising offspring toward Pareto-optimal front (PF) with diversity. Estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators. In this paper, an improved regularity-based estimation of distribution algorithm is proposed to effectively tackle unconstrained MaOPs. In the proposed algorithm,  diversity repairing mechanism  is utilized to mend the areas, where need nondominated solutions with a closer proximity to the PF. Then  favorable solutions  are generated by the model built from the regularity of the solutions surrounding a group of representatives. These two steps collectively enhance the selection pressure which gives rise to the superior convergence of the proposed algorithm. In addition, dimension reduction technique is employed in the decision space to speed up the estimation search of the proposed algorithm. Finally, by assigning the Pareto-optimal solutions to the uniformly distributed reference vectors, a set of solutions with excellent diversity and convergence is obtained. To measure the performance, NSGA-III, GrEA, MOEA/D, HypE, MBN-EDA, and RM-MEDA are selected to perform comparison experiments over DTLZ and DTLZ− test suites with 3-, 5-, 8-, 10-, and 15-objective. Experimental results quantified by the selected performance metrics reveal that the proposed algorithm shows considerable competitiveness in addressing unconstrained MaOPs.

Connected through hyperedge HE-1 (induced by source paper "evolutionary multi objective optimization driven by generative adversarial networks gans"):
  with members:
    - "platemo a matlab platform for evolutionary multi objective optimization"
    - "preselection via classification a case study on evolutionary multiobjective optimization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "automatically designing cnn architectures using genetic algorithm for image classification"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "igd indicator based evolutionary algorithm for many objective optimization problems"
    - "transfer learning based dynamic multiobjective optimization algorithms"
    - "large scale evolution of image classifiers"
    - "skip connections eliminate singularities"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-3 (induced by source paper "automatically evolving cnn architectures based on blocks"):
  with members:
    - "igd indicator based evolutionary algorithm for many objective optimization problems"
    - "skip connections eliminate singularities"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "neural architecture search with reinforcement learning"

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)