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: "geotagging one hundred million twitter accounts with total variation minimization"
  Abstract: Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101, 846, 236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets.

Connected through hyperedge HE-1 (induced by source paper "demographic inference and representative population estimates from multilingual social media data"):
  with members:
    - "on the convergence of adam and beyond"
    - "bleaching text abstract features for cross lingual gender prediction"
    - "co training for demographic classification using deep learning from label proportions"
    - "word translation without parallel data"
    - "densely connected convolutional networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "whom should we sense in social sensing analyzing which users work best for social media now casting"
    - "character aware neural language models"

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 "modeling spatiotemporal factors associated with sentiment on twitter synthesis and suggestions for improving the identification of localized deviations"):
  with members:
    - "the happiness paradox your friends are happier than you"
    - "a survey of location inference techniques on twitter"
    - "what a nasty day exploring mood weather relationship from twitter"
    - "home location identification of twitter users"
    - "comparing and combining sentiment analysis methods"
    - "the dynamics of health behavior sentiments on a large online social network"
    - "assessing vaccination sentiments with online social media implications for infectious disease dynamics and control"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (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"
    - "what the language you tweet says about your occupation"
    - "xgboost a scalable tree boosting system"
    - "detecting the age of twitter users"
    - "diffusion of lexical change in social media"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "the opacity problem in social contagion"):
  with members:
    - "information is not a virus and other consequences of human cognitive limits"
    - "node2vec scalable feature learning for networks"
    - "can cascades be predicted"
    - "distributed representations of words and phrases and their compositionality"
    - "virality prediction and community structure in social networks"
    - "social influence in social advertising evidence from field experiments"
    - "what stops social epidemics"

Connected through hyperedge HE-6 (induced by source paper "computational landscape of user behavior on social media"):
  with members:
    - "computational complexity of the minimum state probabilistic finite state learning problem on finite data sets"
    - "analysis of twitter traffic based on renewal densities"
    - "information transfer in social media"
    - "enumerating finitary processes"

Connected through hyperedge HE-7 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "identifying community structures in dynamic networks"

Connected through hyperedge HE-8 (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"

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.SI(Social and Information Networks)