human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploiting user frequency information for mining regionalisms from social media texts"):
  Abstract: The task of detecting regionalisms (expressions or words used in certain regions) has traditionally relied on the use of questionnaires and surveys, and has also heavily depended on the expertise and intuition of the surveyor. The irruption of Social Media and its microblogging services has produced an unprecedented wealth of content, mainly informal text generated by users, opening new opportunities for linguists to extend their studies of language variation. Previous work on automatic detection of regionalisms depended mostly on word frequencies. In this work, we present a novel metric based on Information Theory that incorporates user frequency. We tested this metric on a corpus of Argentinian Spanish tweets in two ways: via manual annotation of the relevance of the retrieved terms, and also as a feature selection method for geolocation of users. In either case, our metric outperformed other techniques based solely in word frequency, suggesting that measuring the amount of users that produce a word is informative. This tool has helped lexicographers discover several unregistered words of Argentinian Spanish, as well as different meanings assigned to registered words.

Includes members:
  Member "a survey of location prediction on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach")
  Member "continuous representation of location for geolocation and lexical dialectology using mixture density networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation")
  Member "a neural model for user geolocation and lexical dialectology":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic location type classification from social media posts")
  Member "diffusion of lexical change in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "where new words are born distributional semantic analysis of neologisms and their semantic neighborhoods")
  Member "entropy based classification of retweeting activity on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fake cures user centric modeling of health misinformation in social media")

Question: Please tell me which class the source paper 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)