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: "review and evaluation of feature selection algorithms in synthetic problems"
  Abstract: Abstract: The main purpose of Feature Subset Selection is to ﬁnd a reduced subsetof attributes from a data set described by a feature set. The task of a featureselectionalgorithm (FSA) is to provide with a computational solution motivated by a certaindeﬁnition of relevance or by a reliable evaluation measure. In this paper several funda-mental algorithms are studied to assess their performance in a controlled experimentalscenario. A measure to evaluate FSAs is devised that computes the degree of matchingbetween the output given by a FSA and the known optimal solutions. An extensiveexperimental study on synthetic problems is carried out to assess the behaviour ofthe algorithms in terms of solution accuracy and size as a function of the relevance,irrelevance, redundancy and size of the data samples. The controlled experimentalconditions facilitate the derivation of better-supported and meaningful conclusions.Keywords:Feature Selection Algorithms; Empirical Evaluations; Attribute relevanceand redundancy.1 INTRODUCTION

Connected through hyperedge HE-1 (induced by source paper "relief based feature selection introduction and review"):
  with members:
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "xgboost a scalable tree boosting system"

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.AI(Artificial Intelligence)