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: "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
  Abstract: We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.

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"
    - "multiview deep learning for predicting twitter users location"
    - "confounds and consequences in geotagged twitter data"
    - "performance of social network sensors during hurricane sandy"
    - "inferring the origin locations of tweets with quantitative confidence"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-2 (induced by source paper "exploiting user frequency information for mining regionalisms from social media texts"):
  with members:
    - "a survey of location prediction on twitter"
    - "a neural model for user geolocation and lexical dialectology"
    - "diffusion of lexical change in social media"
    - "entropy based classification of retweeting activity on twitter"

Connected through hyperedge HE-3 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "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"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-4 (induced by source paper "semi supervised user geolocation via graph convolutional networks"):
  with members:
    - "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"
    - "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-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"
    - "predicting the industry of users on social media"
    - "exploiting text and network context for geolocation of social media users"
    - "confounds and consequences in geotagged twitter data"

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)