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: "ensemble of support vector machines for land cover classification"
  Abstract: This letter presents the results of two different ensemble approaches to increase the accuracy of land cover classification using support vector machines. Finite ensemble approaches, based on boosting and bagging and infinite ensemble created by embedding the infinite hypothesis in the kernel of support vector machines, are discussed. Results suggest that the infinite ensemble approach provides a significant increase in the classification accuracy in comparison to the radial basis function kernel-based support vector machines. While using finite ensemble approaches, bagging works well and provides a comparable performance to the infinite ensemble approach, whereas boosting decreases the performance of support vector machines. Comparison in terms of computational cost suggests that finite ensemble approaches require a large processing time in comparison to the infinite ensemble approach.

Connected through hyperedge HE-1 (induced by source paper "support vector machines relevance vector machine for remote sensing classification a review"):
  with members:
    - "artificial neural networks and support vector machines for water demand time series forecasting"

Connected through hyperedge HE-2 (induced by source paper "an svm multiclassifier approach to land cover mapping"):
  with members:
    - "image classification using svms one against one vs one against all"

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)