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: "mutual information for the selection of relevant variables in spectrometric nonlinear modelling"
  Abstract: Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of parameters, leading to overfitting and poor generalization abilities. In this paper, we suggest the use of the mutual information measure to select variables from the initial set. The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is thus suitable for nonlinear modelling. In addition, it leads to the selection of variables among the initial set, and not to linear or nonlinear combinations of them. Without decreasing the model performances compared to other variable projection methods, it allows therefore a greater interpretability of the results.

Connected through hyperedge HE-1 (induced by source paper "a deep learning approach for analyzing the composition of chemometric data"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "high resolution talking face generation via mutual information approximation"):
  with members:
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "which training methods for gans do actually converge"
    - "speech driven facial reenactment using conditional generative adversarial networks"
    - "obamanet photo realistic lip sync from text"
    - "ensemble estimation of mutual information"
    - "recurrent scale approximation for object detection in cnn"
    - "you said that"

Connected through hyperedge HE-3 (induced by source paper "advances in feature selection with mutual information"):
  with members:
    - "resampling methods for parameter free and robust feature selection with mutual information"

Connected through hyperedge HE-4 (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"
    - "representation of functional data in neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast selection of spectral variables with b spline compression"):
  with members:
    - "representation of functional data in neural networks"

Connected through hyperedge HE-6 (induced by source paper "resampling methods for parameter free and robust feature selection with mutual information"):
  with members:
    - "representation of functional data in neural networks"

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.LG(Machine Learning)