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: "reconstruction and analysis of cancer specific gene regulatory networks from gene expression profiles"
  Abstract: The main goal of Systems Biology research is to reconstruct biological networks for its topological analysis so that reconstructed networks can be used for the identification of various kinds of disease. The availability of high-throughput data generated by microarray experiments fuelled researchers to use whole-genome gene expression profiles to understand cancer and to reconstruct key cancer -specific gene regulatory network. Now, the researchers are taking a keen interest in the development of algorithm for the reconstruction of gene regulatory network from whole genome expression profiles. In this study, a cancer-specific gene regulatory network (prostate cancer) has been const ructed using a simple and novel statistics based approach. First, significant genes differentially expressing them self in the disease condition has been identified using a two -stage filtering approach t-test and fold-change measure. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. The obtained results has been validated with the available databases and literatures. We obtained a cancer-specific regulatory network of 29 genes with a total o f 55 regulatory relations in which some of the genes has been identified as hub genes that can act as drug target for the cancer diagnosis.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "ant colony optimization for inferring key gene interactions"):
  with members:
    - "evolutionary algorithms in genetic regulatory networks model"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data"):
  with members:
    - "reconstruction of gene regulatory network of colon cancer using information theoretic approach"
    - "a novel anticlustering filtering algorithm for the prediction of genes as a drug target"

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.SY(Systems and Control)