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: "walking through a library remotely why we need maps for collections and how knowescape can help us to make them"
  Abstract: There is no escape from the expansion of information, so that structuring and locating meaningful knowledge becomes ever more difficult. The question of how to order our knowledge is as old as the systematic acquisition, circulation, and storage of knowledge. Classification systems have been known since ancient times. On the Internet, one finds both classifications and taxonomies designed by information professionals and folksonomies based on social tagging. Nevertheless, a user navigating through large information spaces is still confronted with a text based search interface and a list of hits as outcome. There is still an obvious gap between a physical encounter with, for example, a librarys collection and browsing its content through an on-line catalogue. This paper starts from the need of digital scholarship for effective knowledge inquiry, revisits traditional ways to support knowledge ordering and information retrieval, and introduces into a newly funded research network where five different communities from all corners of the scientific landscape join forces in a quest for knowledge maps. It can be read as a manifesto for a newly funded specific research network KnoweScape. At the same time it is a general reflection about what one has to take into account when representing structure and evolution of data, information and knowledge and designing instruments to help scholars and others to navigate across the lands and oceans of knowledge.

Connected through hyperedge HE-1 (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"
    - "searching data a review of observational data retrieval practices in selected disciplines"
    - "the durability and fragility of knowledge infrastructures lessons learned from astronomy"
    - "query expansion for survey question retrieval in the social sciences"

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)