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: "novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm"
  Abstract: Cancer is one of the most feared diseases in the world it has increased disturbingly and breast cancer occurs in one out of eight women, the prediction of malignancies plays essential roles not only in revealing human genome, but also in discovering effective prevention and treatment of cancers. Generally cancer disease driven by somatic mutations in an individual's DNA sequence, or genome that accumulates during the person's lifetime. This paper is proposed a novel method can predict the disease by mutations despite The presence in gene sequence is not necessary it are malignant, so will be compare the patient's protein with the gene's protein of disease if there is difference between these two proteins then can say there is malignant mutations. This method will use bioinformatics techniques like FASTA, CLUSTALW, etc which shows whether malignant mutations or not, then training the back-propagation algorithm using all expected malignant mutations for a certain disease's genes (e.g. BRCA1 and BRCA2), and using it to test whether patient is holder the disease or not. Implementing this novel method as the first way to predict the disease based on mutations in the sequence of the gene that causes the disease shows two decisions are achieved successfully, the first diagnose whether the patient has cancer's mutations or not using bioinformatics techniques the second classifying these mutations are related to breast cancer (e.g. BRCA1and BRCA2) using back-propagation with mean square rate 0.0000001. Keywords-Gene sequence; Protein; Deoxyribonucleic Acid DNA; Malignant mutation; Bioinformatics; Back- propagation algorithm

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"
    - "enhancement of a 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:
    - "enhancement of a 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:
    - "enhancement of a 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)