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: "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
  Abstract: In this work, we compare GDELT and Event Registry, which monitor news articles worldwide and provide big data to researchers regarding scale, news sources, and news geography. We found significant differences in scale and news sources, but surprisingly, we observed high similarity in news geography between the two datasets.

Connected through hyperedge HE-1 (induced by source paper "a dynamic embedding model of the media landscape"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "vista a visually socially and temporally aware model for artistic recommendation"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "what we read what we search media attention and public attention among 193 countries"):
  with members:
    - "what gets media attention and how media attention evolves over time large scale empirical evidence from 196 countries"
    - "multiplex media attention and disregard network among 129 countries"
    - "news across languages cross lingual document similarity and event tracking"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "selection bias in news coverage learning it fighting it"):
  with members:
    - "a tutorial on principal component analysis"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "more voices than ever quantifying media bias in networks"

Connected through hyperedge HE-4 (induced by source paper "revealing the hidden patterns of news photos analysis of millions of news photos using gdelt and deep learning based vision apis"):
  with members:
    - "breaking the news first impressions matter on online news"
    - "understanding news geography and major determinants of global news coverage of disasters"

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)