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: "empowering swarm based optimizers by multi scale search to enhance gradient descent initialization performance"
  Abstract: Swarm-based optimizers like Particle Swarm Optimization or Imperialistic Competitive Algorithm that act under influences of cooperation or competition among groups, are unable to search in multiple volumes of locality or globality and do not have nested localities. As hybrid optimizers, they may not give satisfactory results as initializers in Gradient Descent approximators used in plenty of multimodal problems like nonlinear subspace learning and neural network training, which have hierarchies of convex spaces due to nonlinearity and multi-layer nature of these models. To search in various levels of scale in a homogenous way, a framework is proposed to equip PSO and ICA a multi-scale search capability. Then, the resulted optimizers are evaluated in single and GD-hybridized mode. Hybrid evaluation as GD randomizer is implemented with the help of a nonlinear subspace filtering objective function over EEG data and optimization loss and validation data accuracy is compared with other hybrids containing GD. A single evaluation is also taken place between the proposed ones, PSO, ICA, CLPSO, and CICA, which are used more in hybrid learning-based approaches. Evaluations were with respect to solution error. Before concluding the paper, it is shown and analyzed that proposed optimizers outperform algorithms of related context both in single and hybrid-GD mode.

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)