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: "probing multivariate indicators for academic evaluation"
  Abstract: Since Garfield introduced the journal impact factor (JIF) and set up citation analysis, some scientometric indicators have been applied to academic evaluations. Then Hirsch proposed the h-index, which was rapidly accepted by the scientific community. This promoted the development of quantitative academic indicators. However, both JIF and h-index have their advantages and disadvantages. The Integrated Impact Indicator (I3) and the h-index can be combined into the I3-type framework, yielding the publication vector X =(X1,X2,X3) and the citation vector Y = (Y1,Y2,Y3), the publication score I3X = X1+X2+X3 and the citation score I3Y = Y1+Y2+Y3, and alternative indicators, which could be developed to extend academic evaluation. The vector X and the score I3X represent the relative frequencies of the publications, while the vector Y and the score I3Y denote the relative frequencies of the citations. The h-based I3-type multivariate indicators provide multidimensional indicators: X1 measures publication score in the h-core (X1 and Y1 combination may measure core impact power); X2 measures publication score in h-tail; Yh = Y1+Y3 measures citation score in h-core; Y2 measures citation score in h-tail; I3X does total publication score,and I3Y does total citation score. The empirical studies reveal that the h-core distribution is suitable to evaluate scholars, and the X1 and Y1 are applied to measure core impact power of universities, and I3X and I3Y are alternatives of journal impact factor(JIF). The multivariate indicators provide a multidimensional view of academic evaluation with using the advantages of both the h-index and I3. The publication vector X =(X1,X2,X3) and the citation vector Y = (Y1,Y2,Y3) with their elements, as well as the publication score I3X = X1+X2+X3 and the citation score I3Y = Y1+Y2+Y3, contribute multivariate indicators for academic evaluation.

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)