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: "where in the world are you geolocation and language identification in twitter"
  Abstract: The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets to understand important questions related to the 300 million tweets that flow through the platform each day. Much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced, however. Furthermore, standard, well-accepted practices have yet to emerge. As such, this article studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreemen...

Connected through hyperedge HE-1 (induced by source paper "a hierarchical location prediction neural network for twitter user geolocation"):
  with members:
    - "location order recovery in trails with low temporal resolution"
    - "semi supervised user geolocation via graph convolutional networks"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "attention is all you need"
    - "a neural model for user geolocation and lexical dialectology"
    - "multiview deep learning for predicting twitter users location"
    - "a probabilistic framework for location inference from social media"
    - "inferring the location of authors from words in their texts"

Connected through hyperedge HE-2 (induced by source paper "a large scale empirical study of geotagging behavior on twitter"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "location order recovery in trails with low temporal resolution"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "a probabilistic framework for location inference from social media"
    - "twitter user geolocation using a unified text and network prediction model"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-3 (induced by source paper "infringement of tweets geo location privacy an approach based on graph convolutional neural networks"):
  with members:
    - "a survey of location prediction on twitter"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-4 (induced by source paper "scholarly twitter metrics"):
  with members:
    - "a systematic identification and analysis of scientists on twitter"
    - "grand challenges in altmetrics heterogeneity data quality and dependencies"
    - "botornot a system to evaluate social bots"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "the rise of social bots"
    - "when is an article actually published an analysis of online availability publication and indexation dates"
    - "differences in personal and professional tweets of scholars"
    - "influence of study type on twitter activity for medical research papers"

Connected through hyperedge HE-5 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "density estimation for geolocation via convolutional mixture density network"
    - "a survey of point of interest recommendation in location based social networks"
    - "exploiting text and network context for geolocation of social media users"
    - "analysis of named entity recognition and linking for tweets"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"

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"
    - "confounds and consequences in geotagged twitter data"
    - "computational sociolinguistics a survey"
    - "statistically significant detection of linguistic change"
    - "diffusion of lexical change in social media"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-7 (induced by source paper "on the fly detection of autogenerated tweets"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "making the most of tweet inherent features for social spam detection on twitter"
    - "estimating continuous distributions in bayesian classifiers"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "on predicting geolocation of tweets using convolutional neural networks"):
  with members:
    - "a probabilistic framework for location inference from social media"
    - "inferring the location of authors from words in their texts"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

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.CY(Computers and Society)