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: "what gets media attention and how media attention evolves over time large scale empirical evidence from 196 countries"
  Abstract: It is known that news topics, covered more frequently and over longer periods of time, are considered to be important to the public. Hence, what gets media attention and how media attention evolves over time has been studied for decades in communication study. However, previous studies are confined to a few countries or a few topics, mainly due to lack of longitudinal global data. In this work, we use a large-scale news data compiled from 196 countries to provide empirical analyses of media attention dynamics.

Connected through hyperedge HE-1 (induced by source paper "what we read what we search media attention and public attention among 193 countries"):
  with members:
    - "multiplex media attention and disregard network among 129 countries"
    - "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
    - "news across languages cross lingual document similarity and event tracking"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "an exploration of unreliable news classification in brazil and the u s"):
  with members:
    - "semi supervised content based detection of misinformation via tensor embeddings"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"

Connected through hyperedge HE-3 (induced by source paper "demographics of news sharing in the u s twittersphere"):
  with members:
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "breaking the news first impressions matter on online news"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "learning deep face representation"
    - "influence and passivity in social media"

Connected through hyperedge HE-4 (induced by source paper "multiplex media attention and disregard network among 129 countries"):
  with members:
    - "data driven approach to measuring the level of press freedom using media attention diversity from unfiltered news"
    - "node2vec scalable feature learning for 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.CY(Computers and Society)