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: "searching data a review of observational data retrieval practices in selected disciplines"
  Abstract: A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

Connected through hyperedge HE-1 (induced by source paper "lost or found discovering data needed for research"):
  with members:
    - "dataset search a survey"
    - "digital data archives as knowledge infrastructures mediating data sharing and reuse"
    - "theory and practice of data citation"
    - "understanding data search as a socio technical practice"

Connected through hyperedge HE-2 (induced by source paper "understanding data search as a socio technical practice"):
  with members:
    - "data search 18 searching data on the web"
    - "digital data archives as knowledge infrastructures mediating data sharing and reuse"
    - "the parallel distributed image search engine paradise"
    - "the durability and fragility of knowledge infrastructures lessons learned from astronomy"
    - "walking through a library remotely why we need maps for collections and how knowescape can help us to make them"
    - "query expansion for survey question retrieval in the social sciences"

Connected through hyperedge HE-3 (induced by source paper "data search 18 searching data on the web"):
  with members:
    - "contextualised browsing in a digital library s living lab"
    - "understanding data search as a socio technical practice"
    - "learning latent vector spaces for product search"

Connected through hyperedge HE-4 (induced by source paper "how fair can you get image retrieval as a use case to calculate fair metrics"):
  with members:
    - "a web api ecosystem through feature based reuse"

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)