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: "exploiting text and network context for geolocation of social media users"
  Abstract: Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text- and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets.

Connected through hyperedge HE-1 (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"
    - "a probabilistic framework for location inference from social media"
    - "inferring the location of authors from words in their texts"

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"
    - "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"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-3 (induced by source paper "semi supervised user geolocation via graph convolutional networks"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "multiview deep learning for predicting twitter users location"
    - "density estimation for geolocation via convolutional mixture density network"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-4 (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"
    - "a survey of location inference techniques on twitter"
    - "evolution of reddit from the front page of the internet to a self referential community"

Connected through hyperedge HE-5 (induced by source paper "a comparative analysis of content based geolocation in blogs and tweets"):
  with members:
    - "semi supervised user geolocation via graph convolutional networks"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "predicting the industry of users on social media"
    - "confounds and consequences in geotagged twitter data"

Connected through hyperedge HE-6 (induced by source paper "social sensing of floods in the uk"):
  with members:
    - "a survey of location inference techniques on twitter"
    - "geotagging one hundred million twitter accounts with total variation minimization"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic framework for location inference from social media"):
  with members:
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "wide deep learning for recommender systems"
    - "semi supervised classification with graph convolutional networks"
    - "towards real time country level location classification of worldwide tweets"
    - "tensorflow a system for large scale machine learning"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a neural model for user geolocation and lexical dialectology"):
  with members:
    - "a kernel independence test for geographical language variation"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "theano a python framework for fast computation of mathematical expressions"
    - "confounds and consequences in geotagged twitter data"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)