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: "do altmetrics work for assessing research quality"
  Abstract: Alternative metrics (aka altmetrics) are gaining increasing interest in the scientometrics community as they can capture both the volume and quality of attention that a research work receives online. Nevertheless, there is limited knowledge about their effectiveness as a mean for measuring the impact of research if compared to traditional citation-based indicators. This work aims at rigorously investigating if any correlation exists among indicators, either traditional (i.e. citation count and h-index) or alternative (i.e. altmetrics) and which of them may be effective for evaluating scholars. The study is based on the analysis of real data coming from the National Scientific Qualification procedure held in Italy by committees of peers on behalf of the Italian Ministry of Education, Universities and Research.

Connected through hyperedge HE-1 (induced by source paper "the practice of self citations a longitudinal study"):
  with members:
    - "citation gaming induced by bibliometric evaluation a country level comparative analysis"
    - "open data to evaluate academic researchers an experiment with the italian scientific habilitation"
    - "nine million books and eleven million citations a study of book based scholarly communication using opencitations"
    - "opencitations an infrastructure organization for open scholarship"
    - "assessing evaluation procedures for individual researchers the case of the italian national scientific qualification"
    - "quantitative analysis of the italian national scientific qualification"

Connected through hyperedge HE-2 (induced by source paper "open data to evaluate academic researchers an experiment with the italian scientific habilitation"):
  with members:
    - "do altmetrics correlate with the quality of papers a large scale empirical study based on f1000prime data"
    - "quantitative analysis of the italian national scientific qualification"
    - "an assessment of the first scientific habilitation for university appointments in italy"
    - "the first italian research assessment exercise a bibliometric perspective"
    - "allocative efficiency in public research funding can bibliometrics help"

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)