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: "long term availability of raw experimental data in experimental fracture mechanics"
  Abstract: Abstract   Experimental data availability is a cornerstone for reproducibility in experimental fracture mechanics, which is crucial to the scientific method. This short communication focuses on the accessibility and long term availability of raw experimental data. The corresponding authors of the eleven most cited papers, related to experimental fracture mechanics, for every year from 2000 up to 2016, were kindly asked about the availability of the raw experimental data associated with each publication. For the 187 e-mails sent:     22.46    % resulted in outdated contact information,     57.75    % of the authors did received our request and did not reply, and     19.79     replied to our request. The availability of data is generally low with only 11 available data sets (    5.9    %). The authors identified two main issues for the lacking availability of raw experimental data. First, the ability to retrieve data is strongly attached to the possibility to contact the corresponding author. This study suggests that institutional e-mail addresses are insufficient means for obtaining experimental data sets. Second, lack of experimental data is also due that submission and publication does not require to make the raw experimental data available. The following solutions are proposed: (1) Requirement of unique identifiers, like ORCID or ResearcherID, to detach the author(s) from their institutional e-mail address, (2) Provide DOIs, like Zenodo or Dataverse, to make raw experimental data citable, and (3) grant providing organizations should ensure that experimental data by public funded projects is available to the public.

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)