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 novel anticlustering filtering algorithm for the prediction of genes as a drug target"
  Abstract: The high-throughput data generated by microarray experiments provides complete set of genes being expressed in a g iven cell or in an organis m under part icular conditions. The analysis of these enormous data has opened a new dimension for the researchers. In this paper we describe a novel algorith m to microarray data analysis focusing on the identification of genes that are differentially expressed in particular internal or external conditions and which could be potential drug targets. The algorithm uses the time -series gene expression data as an input and recognizes genes which are expressed differentially. This algorith m imp lements standard statistics-based gene functional investigations, such as the log transformation, mean, log-sig moid function, coefficient of variations, etc. It does not use clustering analysis. The proposed algorith m has been implemented in Perl. The time-series gene exp ression data on yeast Saccharomyces cerevisiae fro m the Stanford Microarray Database (SMD) consisting of 6154 genes have been taken for the validation o f the algorith m. The developed method extracted 48 genes out of total 6154 genes. These genes are mostly responsible for the yeast's resistants at a high temperature.

Connected through hyperedge HE-1 (induced by source paper "analysis of microarray data using artificial intelligence based techniques"):
  with members:
    - "formal concept analysis for knowledge discovery from biological data"
    - "recurrent neural network based hybrid model for reconstructing gene regulatory network"
    - "ant colony optimization for inferring key gene interactions"
    - "reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles"
    - "a brief review of nature inspired algorithms for optimization"
    - "a comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data"
    - "evolutionary algorithms in genetic regulatory networks model"
    - "artificial neural networks for beginners"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data"):
  with members:
    - "reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles"
    - "reconstruction of gene regulatory network of colon cancer using information theoretic approach"

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)