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: "enhancement of a novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm"
  Abstract: The noval method for mutational disease prediction using bioinformatics tools and datasets for diagnosis the malignant mutations with powerful Artificial Neural Network (Backpropagation Network) for classifying these malignant mutations are related to gene(s) (e.g. BRCA1 and BRCA2) cause a disease (breast cancer). This noval method didn't take in consideration just like adopted for dealing, analyzing and treat the gene sequences for extracting useful information from the sequence, also exceeded the environment factors which play important roles in deciding and calculating some of genes features in order to view its functional parts and relations to diseases. This paper is proposed an enhancement of a novel method as a first way for diagnosis and prediction the disease by mutations considering and introducing multi other features show the alternations, changes in the environment as well as genes, comparing sequences to gain information about the structure/function of a query sequence, also proposing optimal and more accurate system for classification and dealing with specific disorder using backpropagation with mean square rate 0.000000001.

Connected through hyperedge HE-1 (induced by source paper "effective data mining technique for classification cancers via mutations in gene using neural network"):
  with members:
    - "novel mining of cancer via mutation in tumor protein p53 using quick propagation network"
    - "novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm"

Connected through hyperedge HE-2 (induced by source paper "novel mining of cancer via mutation in tumor protein p53 using quick propagation network"):
  with members:
    - "novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm"
    - "new approach for prediction pre cancer via detecting mutated in tumor protein p53"

Connected through hyperedge HE-3 (induced by source paper "new approach for prediction pre cancer via detecting mutated in tumor protein p53"):
  with members:
    - "novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm"

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)