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 probabilistic framework for location inference from social media"
  Abstract: We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring. In recent years, a number of algorithms have been proposed for identifying user locations on social media platforms such as Twitter and Facebook from message contents, friend networks, and interactions between users. In this paper, we propose a novel probabilistic model based on factor graphs for location inference that offers several unique advantages for this task. First, the model generalizes previous methods by incorporating content, network, and deep features learned from social context. The model is also flexible enough to support both supervised learning and semi-supervised learning. Second, we explore several learning algorithms for the proposed model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which improves the inference accuracy. Third, we validate the proposed model on three different genres of data - Twitter, Weibo, and Facebook - and demonstrate that the proposed model can substantially improve the inference accuracy (+3.3-18.5% by F1-score) over that of several state-of-the-art methods.

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"
    - "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"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-2 (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"
    - "inferring the location of authors from words in their texts"
    - "exploiting text and network context for geolocation of social media users"

Connected through hyperedge HE-3 (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"
    - "twitter user geolocation using a unified text and network prediction model"
    - "geo located twitter as proxy for global mobility patterns"
    - "where in the world are you geolocation and language identification in twitter"

Connected through hyperedge HE-4 (induced by source paper "city level geolocation of tweets for real time visual analytics"):
  with members:
    - "interactive learning for identifying relevant tweets to support real time situational awareness"
    - "location reference identification from tweets during emergencies a deep learning approach"
    - "end to end network for twitter geolocation prediction and hashing"
    - "multiview deep learning for predicting twitter users location"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "location order recovery in trails with low temporal resolution"):
  with members:
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "on predicting geolocation of tweets using convolutional neural networks"):
  with members:
    - "inferring the location of authors from words in their texts"
    - "adam a method for stochastic optimization"
    - "where in the world are you geolocation and language identification in twitter"
    - "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.AI(Artificial Intelligence)