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: "formal concept analysis for knowledge discovery from biological data"
  Abstract: Owing to the rapid advancement in high-throughput technologies, such as microarrays and next generation sequencing, the volume of biological data is increasing exponentially. The current challenge in computational biology and bioinformatics research is how to analyse these huge raw biological datasets to extract meaningful biological knowledge. Formal concept analysis is a method based on lattice theory and widely used for data analysis, knowledge representation, knowledge discovery and knowledge management across several domains. This paper reviews the applications of formal concept analysis for knowledge discovery from biological data, including gene expression discretisation, gene co-expression mining, gene expression clustering, finding genes in gene regulatory networks, enzyme/protein classifications, binding site classifications, and domain-domain interaction. It also presents a list of FCA-based software tools applied to the biological domain, and covers the challenges and future directions in this field.

Connected through hyperedge HE-1 (induced by source paper "analysis of microarray data using artificial intelligence based techniques"):
  with members:
    - "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"
    - "a novel anticlustering filtering algorithm for the prediction of genes as a drug target"
    - "artificial neural networks for beginners"

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)