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: "medoc a python wrapper to load medline into a local mysql database"
  Abstract: Since the MEDLINE database was released, the number of documents indexed by this entity has risen every year. Several tools have been developed by the National Institutes of Health (NIH) to query this corpus of scientific publications. However, in terms of advances in big data, text-mining and data science, an option to build a local relational database containing all metadata available on MEDLINE would be truly useful to optimally exploit these resources. MEDOC (MEdline DOwnloading Contrivance) is a Python program designed to download data on an FTP and to load all extracted information into a local MySQL database. It took MEDOC 4 days and 17 hours to load the 26 million documents available on this server onto a standard computer. This indexed relational database allows the user to build complex and rapid queries. All fields can thus be searched for desired information, a task that is difficult to accomplish through the PubMed graphical interface. MEDOC is free and publicly available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "doc2vec on the pubmed corpus study of a new approach to generate related articles"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

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)