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: "enhancing keyword correlation for event detection in social networks using svd and k means twitter case study"
  Abstract: Extracting textual features from tweets is a challenging process due to the noisy nature of the content and the weak signal of most of the words used. In this paper, we propose using singular value decomposition (SVD) with clustering to enhance the signals of the textual features in the tweets to improve the correlation with events. The proposed technique applies SVD to the time series vector for each feature to factorize the matrix of feature/day counts, in order to ensure the independence of the feature vectors. Afterwards, the k-means clustering is applied to build a look-up table that maps members of each cluster to the cluster-centroid. The lookup table is used to map each feature in the original data to the centroid of its cluster, then we calculate the sum of the term frequency vectors of all features in each cluster to the term-frequency-vector of the cluster centroid. To test the technique we calculated the correlations of the cluster centroids with the golden standard record (GSR) vector before and after summing the vectors of the cluster members to the centroid-vector. The proposed method is applied to multiple correlation techniques including the Pearson, Spearman, distance correlation and Kendal Tao. The experiments have also considered the different word forms and lengths of the features including keywords, n-grams, skip-grams and bags-of-words. The correlation results are enhanced significantly as the highest correlation scores have increased from 0.3 to 0.6, and the average correlation scores have increased from 0.3 to 0.4.

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.SI(Social and Information Networks)