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: "mapping the americanization of english in space and time"
  Abstract: As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.

Connected through hyperedge HE-1 (induced by source paper "hahahahaha duuuuude yeeessss a two parameter characterization of stretchable words and the dynamics of mistypings and misspellings"):
  with members:
    - "english verb regularization in books and tweets"
    - "diffusion of lexical change in social media"

Connected through hyperedge HE-2 (induced by source paper "a standardized project gutenberg corpus for statistical analysis of natural language and quantitative linguistics"):
  with members:
    - "natural language statistical features of lstm generated texts"
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "quantifying structure differences in literature using symbolic diversity and entropy criteria"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "text mixing shapes the anatomy of rank frequency distributions"
    - "rank diversity of languages generic behavior in computational linguistics"
    - "a practical approach to language complexity a wikipedia case study"
    - "collaborative tagging and semiotic dynamics"

Connected through hyperedge HE-3 (induced by source paper "english verb regularization in books and tweets"):
  with members:
    - "scaling laws in geo located twitter data"
    - "is language evolution grinding to a halt the scaling of lexical turbulence in english fiction suggests it is not"

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.CL(Computation and Language)