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: "an introduction to the europe media monitor family of applications"
  Abstract: Most large organizations have dedicated departments that monitor the media to keep up-to-date with relevant developments and to keep an eye on how they are represented in the news. Part of this media monitoring work can be automated. In the European Union with its 23 official languages, it is particularly important to cover media reports in many languages in order to capture the complementary news content published in the different countries. It is also important to be able to access the news content across languages and to merge the extracted information. We present here the four publicly accessible systems of the Europe Media Monitor (EMM) family of applications, which cover between 19 and 50 languages (see this http URL). We give an overview of their functionality and discuss some of the implications of the fact that they cover quite so many languages. We discuss design issues necessary to be able to achieve this high multilinguality, as well as the benefits of this multilinguality.

Connected through hyperedge HE-1 (induced by source paper "a pragmatic guide to geoparsing evaluation"):
  with members:
    - "a practical guide for the effective evaluation of twitter user geolocation"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "probing neural network comprehension of natural language arguments"
    - "design challenges and misconceptions in neural sequence labeling"
    - "a coherent unsupervised model for toponym resolution"
    - "a survey of location prediction on twitter"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "the anatomy of brexit debate on facebook"):
  with members:
    - "the topological shape of brexit"
    - "debunking in a world of tribes"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "emotional dynamics in the age of misinformation"
    - "viral misinformation the role of homophily and polarization"
    - "characterizing and curating conversation threads expansion focus volume re entry"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "e3 keyphrase based news event exploration engine"):
  with members:
    - "scalable topical phrase mining from text corpora"

Connected through hyperedge HE-4 (induced by source paper "onts optima news translation system"):
  with members:
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"
    - "empirical methods for compound splitting"

Connected through hyperedge HE-5 (induced by source paper "the anatomy of a modular system for media content analysis"):
  with members:
    - "says who automatic text based content analysis of television news"

Connected through hyperedge HE-6 (induced by source paper "finite time analysis of kernelised contextual bandits"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-7 (induced by source paper "a survey of methods to ease the development of highly multilingual text mining applications"):
  with members:
    - "sentiment analysis in the news"
    - "extending an information extraction tool set to central and eastern european languages"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"

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)