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: "faculty citation measures are highly correlated with peer assessment of computer science doctoral programs"
  Abstract: We study relationship between peer assessment of quality of U.S. Computer Science (CS) doctoral programs and objective measures of research strength of those programs. In Fall 2016 we collected Google Scholar citation data for 4,352 tenure-track CS faculty from 173 U.S. universities. The citations are measured by the t10 index, which represents the number of citations received by the 10th highest cited paper of a faculty. To measure the research strength of a CS doctoral program we use 2 groups of citation measures. The first group of measures averages t10 of faculty in a program. Pearson correlation of those measures with the peer assessment of U.S. CS doctoral programs published by the U.S. News in 2014 is as high as 0.890. The second group of measures counts the number of well cited faculty in a program. Pearson correlation of those measures with the peer assessment is as high as 0.909. By combining those two groups of measures using linear regression, we create the Scholar score whose Pearson correlation with the peer assessment is 0.933 and which explains 87.2% of the variance in the peer assessment. Our evaluation shows that the highest 62 ranked CS doctoral programs by the U.S. News peer assessment are much higher correlated with the Scholar score than the next 57 ranked programs, indicating the deficiencies of peer assessment of less-known CS programs. Our results also indicate that university reputation might have a sizeable impact on peer assessment of CS doctoral programs. To promote transparency, the raw data and the codes used in this study are made available to research community at this http URL

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)