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: "patent portfolio analysis of cities statistics and maps of technological inventiveness"
  Abstract: Cities are engines of the knowledge-based economy, because they are the primary sites of knowledge production activities that subsequently shape the rate and direction of technological change and economic growth. Patents provide a wealth of information to analyse the knowledge specialization at specific places, such as technological details and information on inventors and entities involved, including address information. The technology codes on each patent document indicate the specialization and scope of the underlying technological knowledge of a given invention. In this paper we introduce tools for portfolio analysis in terms of patents that provide insights into the technological specialization of cities. The mapping and analysis of patent portfolios of cities using data of the Unites States Patent and Trademark Office (USPTO) website (at this http URL) and dedicated tools (at this http URL) can be used to analyse the specialisation patterns of inventive activities among cities. The results allow policy makers and other stakeholders to identify promising areas of further knowledge development and 'smart specialisation' strategies.

Connected through hyperedge HE-1 (induced by source paper "mapping patent classifications portfolio and statistical analysis and the comparison of strengths and weaknesses"):
  with members:
    - "measuring technological distance for patent mapping"
    - "strategic intelligence on emerging technologies scientometric overlay mapping"
    - "generating clustered journal maps an automated system for hierarchical classification"
    - "journal portfolio analysis for countries cities and organizations maps and comparisons"
    - "patents as instruments for exploring innovation dynamics geographic and technological perspectives on photovoltaic cells"
    - "can technology life cycles be indicated by diversity in patent classifications the crucial role of variety"
    - "science visualization and discursive knowledge"
    - "a unified approach to mapping and clustering of bibliometric networks"

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)