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: "a feature subset selection algorithm automatic recommendation method"
  Abstract: Many feature subset selection (FSS) algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate FSS algorithms for the problem at hand. Thus, FSS algorithm automatic recommendation is very important and practically useful. In this paper, a meta learning based FSS algorithm automatic recommendation method is presented. The proposed method first identifies the data sets that are most similar to the one at hand by the k-nearest neighbor classification algorithm, and the distances among these data sets are calculated based on the commonly-used data set characteristics. Then, it ranks all the candidate FSS algorithms according to their performance on these similar data sets, and chooses the algorithms with best performance as the appropriate ones. The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features. The proposed recommendation method is extensively tested on 115 real world data sets with 22 well-known and frequently-used different FSS algorithms for five representative classifiers. The results show the effectiveness of our proposed FSS algorithm recommendation method.

Connected through hyperedge HE-1 (induced by source paper "feature selection and feature extraction in pattern analysis a literature review"):
  with members:
    - "pixel level alignment of facial images for high accuracy recognition using ensemble of patches"
    - "graying the black box understanding dqns"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "a review of feature selection methods based on mutual information"

Connected through hyperedge HE-2 (induced by source paper "concrete autoencoders for differentiable feature selection and reconstruction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "feature selection a data perspective"
    - "autoencoder feature selector"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-3 (induced by source paper "characterizing classification datasets a study of meta features for meta learning"):
  with members:
    - "how complex is your classification problem a survey on measuring classification complexity"
    - "openml networked science in machine learning"
    - "learning heterogeneous similarity measures for hybrid recommendations in meta mining"

Connected through hyperedge HE-4 (induced by source paper "meta learning for construction of resampling recommendation systems"):
  with members:
    - "smote synthetic minority over sampling technique"

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)