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: "evolutionary algorithms in genetic regulatory networks model"
  Abstract: Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.

Connected through hyperedge HE-1 (induced by source paper "fuzzy logic based approaches for gene regulatory network inference"):
  with members:
    - "recurrent neural network based hybrid model for reconstructing gene regulatory network"
    - "analysis of microarray data using artificial intelligence based techniques"
    - "reconstruction of gene regulatory network of colon cancer using information theoretic approach"

Connected through hyperedge HE-2 (induced by source paper "recurrent neural network based hybrid model for reconstructing gene regulatory network"):
  with members:
    - "ant colony optimization for inferring key gene interactions"

Connected through hyperedge HE-3 (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"
    - "a novel anticlustering filtering algorithm for the prediction of genes as a drug target"
    - "artificial neural networks for beginners"

Connected through hyperedge HE-4 (induced by source paper "ant colony optimization for inferring key gene interactions"):
  with members:
    - "reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles"

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)