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: "resampling methods for parameter free and robust feature selection with mutual information"
  Abstract: Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. However, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable. This paper proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic data set as well as on the real-world examples.

Connected through hyperedge HE-1 (induced by source paper "feature relevance determination for ordinal regression in the context of feature redundancies and privileged information"):
  with members:
    - "delayed impact of fair machine learning"
    - "solving linear programs in the current matrix multiplication time"
    - "equality of opportunity in supervised learning"
    - "on the consistency of ordinal regression methods"

Connected through hyperedge HE-2 (induced by source paper "simple stopping criteria for information theoretic feature selection"):
  with members:
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "deeppink reproducible feature selection in deep neural networks"
    - "challenges of feature selection for big data analytics"
    - "a review of feature selection methods based on mutual information"
    - "measures of entropy from data using infinitely divisible kernels"

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

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)