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: "plagiarism detection in arxiv"
  Abstract: We describe a large-scale application of methods for finding plagiarism in research document collections. The methods are applied to a collection of 284,834 documents collected by arXiv.org over a 14 year period, covering a few different research disciplines. The methodology effi- ciently detects a variety of problematic author behaviors, and heuristics are developed to reduce the number of false positives. The methods are also efficient enough to imple- ment as a real-time submission screen for a collection many times larger.

Connected through hyperedge HE-1 (induced by source paper "the demise of single authored publications in computer science a citation network analysis"):
  with members:
    - "estimate and replace a novel approach to integrating deep neural networks with existing applications"
    - "three practical field normalised alternative indicator formulae for research evaluation"

Connected through hyperedge HE-2 (induced by source paper "can microsoft academic assess the early citation impact of in press articles a multi discipline exploratory analysis"):
  with members:
    - "microsoft academic automatic document searches accuracy for journal articles and suitability for citation analysis"
    - "microsoft academic a multidisciplinary comparison of citation counts with scopus and mendeley for 29 journals"
    - "citation analysis with microsoft academic"
    - "three practical field normalised alternative indicator formulae for research evaluation"
    - "influencia de la fecha de publicacion online de los articulos cientificos en los indicadores bibliometricos"
    - "the coverage of microsoft academic analyzing the publication output of a university"
    - "when is an article actually published an analysis of online availability publication and indexation dates"
    - "more precise methods for national research citation impact comparisons"

Connected through hyperedge HE-3 (induced by source paper "efficient 2 step protocol and its discriminative feature selections in secure similar document detection"):
  with members:
    - "machine learning in automated text categorization"

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.DB(Databases)