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: "the polylingual labeled topic model"
  Abstract: In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA. The model accounts for multiple languages with separate topic distributions for each language while restricting the permitted topics of a document to a set of predefined labels. We explore the properties of the model in a two-language setting on a dataset from the social science domain. Our experiments show that our model outperforms LDA and Labeled LDA in terms of their held-out perplexity and that it produces semantically coherent topics which are well interpretable by human subjects.

Connected through hyperedge HE-1 (induced by source paper "ilcm a virtual research infrastructure for large scale qualitative data"):
  with members:
    - "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "a tidy data model for natural language processing using cleannlp"
    - "when politicians talk assessing online conversational practices of political parties on twitter"

Connected through hyperedge HE-2 (induced by source paper "measuring motivations of crowdworkers the multidimensional crowdworker motivation scale"):
  with members:
    - "characterizing the global crowd workforce a cross country comparison of crowdworker demographics"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-3 (induced by source paper "a system for probabilistic linking of thesauri and classification systems"):
  with members:
    - "enriching ontologies with encyclopedic background knowledge for document indexing"
    - "thesoz a skos representation of the thesaurus for the social sciences"
    - "improving retrieval results with discipline specific query expansion"

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)