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: "a survey of location inference techniques on twitter"
  Abstract: The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author's location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features.

Connected through hyperedge HE-1 (induced by source paper "location reference identification from tweets during emergencies a deep learning approach"):
  with members:
    - "a survey of location prediction on twitter"
    - "savitr a system for real time location extraction from microblogs during emergencies"
    - "multiview deep learning for predicting twitter users location"
    - "a probabilistic framework for location inference from social media"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "rapid classification of crisis related data on social networks using convolutional neural networks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (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"
    - "what a nasty day exploring mood weather relationship from twitter"
    - "home location identification of twitter users"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "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-3 (induced by source paper "high resolution home location prediction from tweets using deep learning with dynamic structure"):
  with members:
    - "a survey of location prediction on twitter"
    - "home location identification of twitter users"

Connected through hyperedge HE-4 (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-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"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-6 (induced by source paper "fake news identification on twitter with hybrid cnn and rnn models"):
  with members:
    - "lstm a search space odyssey"
    - "cnn rnn a unified framework for multi label image classification"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"
    - "tweetcred real time credibility assessment of content on twitter"
    - "deep visual semantic alignments for generating image descriptions"
    - "phishari automatic realtime phishing detection on twitter"

Connected through hyperedge HE-7 (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"
    - "confounds and consequences in geotagged twitter data"
    - "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-8 (induced by source paper "geocoding without geotags a text based approach for reddit"):
  with members:
    - "a neural model for user geolocation and lexical dialectology"
    - "multiview deep learning for predicting twitter users location"
    - "exploiting text and network context for geolocation of social media users"
    - "evolution of reddit from the front page of the internet to a self referential community"

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)