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: "inferring the origin locations of tweets with quantitative confidence"
  Abstract: Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.

Connected through hyperedge HE-1 (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"
    - "confounds and consequences in geotagged twitter data"
    - "performance of social network sensors during hurricane sandy"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "determine the user country of a tweet"):
  with members:
    - "home location identification of twitter users"

Connected through hyperedge HE-4 (induced by source paper "on the accuracy of hyper local geotagging of social media content"):
  with members:
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "home location identification of twitter users"
    - "newspad designing for collaborative storytelling in neighborhoods"
    - "geotagging one hundred million twitter accounts with total variation minimization"

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)