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: "patterns of text reuse in a scientific corpus"
  Abstract: We consider the incidence of text “reuse” by researchers via a systematic pairwise comparison of the text content of all articles deposited to arXiv.org from 1991 to 2012. We measure the global frequencies of three classes of text reuse and measure how chronic text reuse is distributed among authors in the dataset. We infer a baseline for accepted practice, perhaps surprisingly permissive compared with other societal contexts, and a clearly delineated set of aberrant authors. We find a negative correlation between the amount of reused text in an article and its influence, as measured by subsequent citations. Finally, we consider the distribution of countries of origin of articles containing large amounts of reused text.

Connected through hyperedge HE-1 (induced by source paper "wikipedia text reuse within and without"):
  with members:
    - "variational deep semantic hashing for text documents"
    - "global disease monitoring and forecasting with wikipedia"
    - "identifying duplicate and contradictory information in wikipedia"

Connected through hyperedge HE-2 (induced by source paper "citation sentence reuse behavior of scientists a case study on massive bibliographic text dataset of computer science"):
  with members:
    - "ocr a robust framework for information extraction from scholarly articles"

Connected through hyperedge HE-3 (induced by source paper "preprint deja vu"):
  with members:
    - "arxiv 25 key findings of a user survey"
    - "positional effects on citation and readership in arxiv"

Connected through hyperedge HE-4 (induced by source paper "comparing the topological properties of real and artificially generated scientific manuscripts"):
  with members:
    - "probing the topological properties of complex networks modeling short written texts"
    - "a systematic comparison of supervised classifiers"
    - "measuring structural distances between texts"
    - "discriminating word senses with tourist walks in complex networks"
    - "scientific impact evaluation and the effect of self citations mitigating the bias by discounting h index"
    - "a practical approach to language complexity a wikipedia case study"
    - "the similarity metric"

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)