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: "probability and expected frequency of breakthroughs a robust method of research assessment based on the double rank property of citation distributions"
  Abstract: In research policy, effective measures that lead to improvements in the generation of knowledge must be based on reliable methods of research assessment, but for many countries and institutions this is not the case. Publication and citation analyses can be used to estimate the part played by countries and institutions in the global progress of knowledge, but a concrete method of estimation is far from evident. The challenge arises because publications that report real progress of knowledge form an extremely low proportion of all publications; in most countries and institutions such contributions appear less than once per year. One way to overcome this difficulty is to calculate probabilities instead of counting the rare events on which scientific progress is based. This study reviews and summarizes several recent publications, and adds new results that demonstrate that the citation distribution of normal publications allows the probability of the infrequent events that support the progress of knowledge to be calculated.

Connected through hyperedge HE-1 (induced by source paper "evaluating research and researchers by the journal impact factor is it better than coin flipping"):
  with members:
    - "systematic analysis of agreement between metrics and peer review in the uk ref"
    - "core elements in the process of citing publications conceptual overview of the literature"
    - "use of the journal impact factor for assessing individual articles need not be wrong"
    - "can the journal impact factor be used as a criterion for the selection of junior researchers a large scale empirical study based on researcherid data"
    - "citation success index an intuitive pair wise journal comparison metric"
    - "citation count distributions for large monodisciplinary journals"
    - "citations indicators of quality the impact fallacy"
    - "a review of the literature on citation impact indicators"

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)