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: "comparing titles vs full text for multi label classification of scientific papers and news articles"
  Abstract: Until today there has been no systematic comparison of how far document classification can be conducted using just the titles of the documents. However, methods using only the titles are very important since automated processing of titles has no legal barriers. Copyright laws often hinder automated document classification on full-text and even abstracts. In this paper, we compare established methods like Bayes, Rocchio, kNN, SVM, and logistic regression as well as recent methods like Learning to Rank and neural networks to the multi-label document classification problem. We demonstrate that classifications solely using the documents' titles can be very good and very close to the classification results using full-text. We use two established news corpora and two scientific document collections. The experiments are large-scale in terms of documents per corpus (up to 100,000) as well as number of labels (up to 10,000). The best method on title data is a modern variant of neural networks. For three datasets, the difference to full-text is very small. For one dataset, a stacking of logistic regression and decision trees performs slightly better than neural networks. Furthermore, we observe that the best methods on titles are even better than several state-of-the-art methods on full-text.

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.DL(Digital Libraries)