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 neural model for user geolocation and lexical dialectology"
  Abstract: We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.

Connected through hyperedge HE-1 (induced by source paper "automatic location type classification from social media posts"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "neural structural correspondence learning for domain adaptation"
    - "character level convolutional networks for text classification"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "named entity recognition with bidirectional lstm cnns"
    - "document embedding with paragraph vectors"
    - "convolutional neural networks for sentence classification"

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"
    - "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"
    - "exploiting text and network context for geolocation of social media users"

Connected through hyperedge HE-3 (induced by source paper "exploiting user frequency information for mining regionalisms from social media texts"):
  with members:
    - "a survey of location prediction on twitter"
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "diffusion of lexical change in social media"
    - "entropy based classification of retweeting activity on twitter"

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

Connected through hyperedge HE-5 (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"
    - "multiview deep learning for predicting twitter users location"
    - "density estimation for geolocation via convolutional mixture density network"
    - "exploiting text and network context for geolocation of social media users"
    - "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-6 (induced by source paper "twitter user geolocation using deep multiview learning"):
  with members:
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "geocoding without geotags a text based approach for reddit"):
  with members:
    - "multiview deep learning for predicting twitter users location"
    - "exploiting text and network context for geolocation of social media users"
    - "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-8 (induced by source paper "multiview deep learning for predicting twitter users location"):
  with members:
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "end to end network for twitter geolocation prediction and hashing"
    - "node2vec scalable feature learning for networks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "addressing the rare word problem in neural machine translation"
    - "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.CL(Computation and Language)