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: "alternative restart strategies for cma es"
  Abstract: This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.

Connected through hyperedge HE-1 (induced by source paper "diagonal acceleration for covariance matrix adaptation evolution strategies"):
  with members:
    - "lm cma an alternative to l bfgs for large scale black box optimization"
    - "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"
    - "efficient natural evolution strategies"

Connected through hyperedge HE-2 (induced by source paper "enhancing gaussian estimation of distribution algorithm by exploiting evolution direction with archive"):
  with members:
    - "scaling up estimation of distribution algorithms for continuous optimization"

Connected through hyperedge HE-3 (induced by source paper "sgdr stochastic gradient descent with restarts"):
  with members:
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "snapshot ensembles train 1 get m for free"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep pyramidal residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "maximum likelihood based online adaptation of hyper parameters in cma es"):
  with members:
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"

Connected through hyperedge HE-5 (induced by source paper "black box optimization benchmarking of ipop saacm es and bipop saacm es on the bbob 2012 noiseless testbed"):
  with members:
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"

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)