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: "representation of functional data in neural networks"
  Abstract: Functional data analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the radial-basis function networks (RBFN) and multi-layer perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, functional principal component analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.

Connected through hyperedge HE-1 (induced by source paper "a data driven functional projection approach for the selection of feature ranges in spectra with ica or cluster analysis"):
  with members:
    - "fast selection of spectral variables with b spline compression"
    - "mutual information for the selection of relevant variables in spectrometric nonlinear modelling"

Connected through hyperedge HE-2 (induced by source paper "multi layer perceptrons and symbolic data"):
  with members:
    - "functional multi layer perceptron a non linear tool for functional data analysis"

Connected through hyperedge HE-3 (induced by source paper "fast selection of spectral variables with b spline compression"):
  with members:
    - "mutual information for the selection of relevant variables in spectrometric nonlinear modelling"

Connected through hyperedge HE-4 (induced by source paper "resampling methods for parameter free and robust feature selection with mutual information"):
  with members:
    - "mutual information for the selection of relevant variables in spectrometric nonlinear modelling"

Connected through hyperedge HE-5 (induced by source paper "theoretical properties of projection based multilayer perceptrons with functional inputs"):
  with members:
    - "functional multi layer perceptron a non linear tool for functional data analysis"

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)