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: "co authorship networks in the digital library research community"
  Abstract: The field of digital libraries (DLs) coalesced in 1994: the first digital library conferences were held that year, awareness of the World Wide Web was accelerating, and the National Science Foundation awarded $24 Million (U.S.) for the Digital Library Initiative (DLI). In this paper we examine the state of the DL domain after a decade of activity by applying social network analysis to the co-authorship network of the past ACM, IEEE, and joint ACM/IEEE digital library conferences. We base our analysis on a common binary undirectional network model to represent the co-authorship network, and from it we extract several established network measures. We also introduce a weighted directional network model to represent the co-authorship network, for which we define $AuthorRank$ as an indicator of the impact of an individual author in the network. The results are validated against conference program committee members in the same period. The results show clear advantages of PageRank and AuthorRank over degree, closeness and betweenness centrality metrics. We also investigate the amount and nature of international participation in Joint Conference on Digital Libraries (JCDL).

Connected through hyperedge HE-1 (induced by source paper "convexity in scientific collaboration networks"):
  with members:
    - "convex skeletons of complex networks"
    - "convexity in complex networks"
    - "constructing bibliometric networks a comparison between full and fractional counting"
    - "distortive effects of initial based name disambiguation on measurements of large scale coauthorship networks"
    - "full and fractional counting in bibliometric networks"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-2 (induced by source paper "analysis of coauthorship network in political science using centrality measures"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-3 (induced by source paper "a survey on expert recommendation in community question answering"):
  with members:
    - "a deep learning approach for expert identification in question answering communities"
    - "xgboost a scalable tree boosting system"
    - "characterization of experts in crowdsourcing platforms"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-4 (induced by source paper "centrality measures for graphons accounting for uncertainty in networks"):
  with members:
    - "graphons a nonparametric method to model estimate and design algorithms for massive networks"
    - "distributions of centrality on networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "pagerank beyond the web"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "building the brazilian academic genealogy tree"):
  with members:
    - "the h index paradox your coauthors have a higher h index than you do"

Connected through hyperedge HE-6 (induced by source paper "coauthorship networks a directed network approach considering the order and number of coauthors"):
  with members:
    - "an empirical analysis of the use of alphabetical authorship in scientific publishing"
    - "applying weighted pagerank to author citation networks"
    - "applying centrality measures to impact analysis a coauthorship network analysis"
    - "discovering author impact a pagerank perspective"
    - "on the relationship between the structural and socioacademic communities of a coauthorship network"

Connected through hyperedge HE-7 (induced by source paper "do pagerank based author rankings outperform simple citation counts"):
  with members:
    - "applying weighted pagerank to author citation networks"
    - "pagerank for ranking authors in co citation networks"
    - "weighted citation an indicator of an article s prestige"
    - "discovering author impact a pagerank perspective"

Connected through hyperedge HE-8 (induced by source paper "learning to rank academic experts in the dblp dataset"):
  with members:
    - "learning to rank for expert search in digital libraries of academic publications"

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)