human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fuzzy logic based approaches for gene regulatory network inference"):
  Abstract: The rapid advancement in high-throughput techniques has fueled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery - which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.

Includes members:
  Member "recurrent neural network based hybrid model for reconstructing gene regulatory network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of microarray data using artificial intelligence based techniques")
  Member "analysis of microarray data using artificial intelligence based techniques":
    also connected through other hyperedges:
      (none)
  Member "reconstruction of gene regulatory network of colon cancer using information theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data")
  Member "evolutionary algorithms in genetic regulatory networks model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recurrent neural network based hybrid model for reconstructing gene regulatory network")

Question: Please tell me which class the source paper 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)