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: "confounds and consequences in geotagged twitter data"
  Abstract: Twitter is often used in quantitative studies that identify geographically-preferred topics, writing styles, and entities. These studies rely on either GPS coordinates attached to individual messages, or on the user-supplied location field in each profile. In this paper, we compare these data acquisition techniques and quantify the biases that they introduce; we also measure their effects on linguistic analysis and text-based geolocation. GPS-tagging and self-reported locations yield measurably different corpora, and these linguistic differences are partially attributable to differences in dataset composition by age and gender. Using a latent variable model to induce age and gender, we show how these demographic variables interact with geography to affect language use. We also show that the accuracy of text-based geolocation varies with population demographics, giving the best results for men above the age of 40.

Connected through hyperedge HE-1 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "sampling from social networks with attributes"

Connected through hyperedge HE-2 (induced by source paper "still out there modeling and identifying russian troll accounts on twitter"):
  with members:
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "unsupervised machine learning of open source russian twitter data reveals global scope and operational characteristics"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "who falls for online political manipulation"
    - "the spread of low credibility content by social bots"
    - "the spread of fake news by social bots"

Connected through hyperedge HE-3 (induced by source paper "author profiling for hate speech detection"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "deep learning for user comment moderation"
    - "utcnn a deep learning model of stance classificationon on social media text"
    - "node2vec scalable feature learning for networks"
    - "ex machina personal attacks seen at scale"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-4 (induced by source paper "a practical guide for the effective evaluation of twitter user geolocation"):
  with members:
    - "a survey of location prediction on twitter"
    - "semi supervised user geolocation via graph convolutional networks"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "multiview deep learning for predicting twitter users location"
    - "performance of social network sensors during hurricane sandy"
    - "inferring the origin locations of tweets with quantitative confidence"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-5 (induced by source paper "correcting sociodemographic selection biases for accurate population prediction from social media"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "controlling for unobserved confounds in classification using correlational constraints"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "demographic dialectal variation in social media a case study of african american english"

Connected through hyperedge HE-6 (induced by source paper "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"):
  with members:
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "a survey of location inference techniques on twitter"
    - "computational sociolinguistics a survey"
    - "statistically significant detection of linguistic change"
    - "where in the world are you geolocation and language identification in twitter"
    - "diffusion of lexical change in social media"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-7 (induced by source paper "mapping the americanization of english in space and time"):
  with members:
    - "dialectometric analysis of language variation in twitter"
    - "demographic dialectal variation in social media a case study of african american english"
    - "is language evolution grinding to a halt the scaling of lexical turbulence in english fiction suggests it is not"
    - "computational sociolinguistics a survey"
    - "diffusion of lexical change in social media"

Connected through hyperedge HE-8 (induced by source paper "a comparative analysis of content based geolocation in blogs and tweets"):
  with members:
    - "semi supervised user geolocation via graph convolutional networks"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "predicting the industry of users on social media"
    - "exploiting text and network context for geolocation of social media users"

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)