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: "what the language you tweet says about your occupation"
  Abstract: Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.

Connected through hyperedge HE-1 (induced by source paper "social media based user embedding a literature review"):
  with members:
    - "attributed social network embedding"
    - "twitter user geolocation using deep multiview learning"
    - "quantifying mental health from social media with neural user embeddings"
    - "node2vec scalable feature learning for networks"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "deepwalk online learning of social representations"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "location occupation and semantics based socioeconomic status inference on twitter"):
  with members:
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "correlations and dynamics of consumption patterns in social economic networks"
    - "predicting twitter user socioeconomic attributes with network and language information"
    - "xgboost a scalable tree boosting system"
    - "detecting the age of twitter users"
    - "diffusion of lexical change in social media"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "analyzing the hate and counter speech accounts on twitter"):
  with members:
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "thou shalt not hate countering online hate speech"
    - "peer to peer hate hate speech instigators and their targets"
    - "like sheep among wolves characterizing hateful users on twitter"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "measuring gamergate a tale of hate sexism and bullying"

Connected through hyperedge HE-4 (induced by source paper "automatically infer human traits and behavior from social media data"):
  with members:
    - "personalized emphasis framing for persuasive message generation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "mining online social data for detecting social network mental disorders"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-6 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"):
  with members:
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "deep residual learning for image recognition"
    - "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "the digital life of walkable streets"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "a world of difference divergent word interpretations among people"):
  with members:
    - "tales of two cities using social media to understand idiosyncratic lifestyles in distinctive metropolitan areas"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "you tweet what you eat studying food consumption through twitter"

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)