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: "utilising deep learning and genome wide association studies for epistatic driven preterm birth classification in african american women"
  Abstract: Genome Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. GWAS typically use a p-value threshold of 5 x 10-8 to identify highly ranked single nucleotide polymorphisms (SNPs). However, evidence has shown that many of these are, in fact, false positives. Using lower p-values it is possible to to investigate the joint epistatic interactions between SNPs and provide better insights into phenotype expression. However, computational complexity is increased exponentially as a function of higher-order combinations. In this paper, we propose a novel framework, based on nonlinear transformations of combinatorically large SNP data, using stacked autoencoders, to identify higher-order SNP interactions. We focus on the challenging problem of classifying preterm births. Evidence suggests that this complex condition has a strong genetic component with unexplained heritability reportedly between 20%-40%. This claim is substantiated using a GWAS data set, obtained from dbGap, which contains predominantly urban low-income African-American women who had normal deliveries (between 37 and 42 weeks of gestation) and preterm deliveries (less than 37 weeks of gestation). Latent representations from original SNP sequences are used to initialize a deep learning classifier before it is fine-tuned for classification tasks (term and preterm births). The complete network models the epistatic effects of major and minor SNP perturbations. All models are evaluated using standard binary classifier performance metrics. The findings show that important information pertaining to SNPs and epistasis can be extracted from 4666 raw SNPs generated using logistic regression (p-value=5 x 10-3) and used to fit a deep learning model and obtain results (Sen=0.9289, Spec=0.9591, Gini=0.9651, Logloss=0.3080, AUC=0.9825, MSE=0.0942) using 500 hidden nodes.

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.CE(Computational Engineering, Finance, and Science)