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: "turning high dimensional optimization into computationally expensive optimization"
  Abstract: Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple low-dimensional subproblems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and subproblems makes it nontrivial to precisely assess the quality of a candidate solution to a subproblem, which has been a major hurdle for applying the idea of DC to nonseparable high-dimensional optimization problems. In this paper, we suggest that searching a good solution to a subproblem can be viewed as a computationally expensive problem and can be addressed with the aid of meta-models. As a result, a novel approach, namely self-evaluation evolution (SEE) is proposed. Empirical studies have shown the advantages of SEE over four representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analyzed in the empirical studies.

Connected through hyperedge HE-1 (induced by source paper "negatively correlated search as a parallel exploration search strategy"):
  with members:
    - "negatively correlated search"

Connected through hyperedge HE-2 (induced by source paper "evolutionary generative adversarial networks"):
  with members:
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "deep semantic hashing with generative adversarial networks"
    - "stabilizing gan training with multiple random projections"
    - "spectral normalization for generative adversarial networks"
    - "vital visual tracking via adversarial learning"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "evolving deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "a parallel divide and conquer based evolutionary algorithm for large scale optimization"):
  with members:
    - "running time analysis of the 1 1 ea for onemax and leadingones under bit wise noise"

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.AI(Artificial Intelligence)