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: "automatic language identification in texts a survey"
  Abstract: Language identification (LI) is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used so far in the LI literature. For describing the features and methods we introduce a unified notation. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

Connected through hyperedge HE-1 (induced by source paper "semi supervised stochastic multi domain learning using variational inference"):
  with members:
    - "implicit reparameterization gradients"
    - "auto encoding variational neural machine translation"
    - "generative neural machine translation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "fixing a broken elbo"
    - "discovering discrete latent topics with neural variational inference"
    - "variational neural machine translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-2 (induced by source paper "dianet bert and hierarchical attention multi task learning of fine grained dialect"):
  with members:
    - "arap tweet a large multi dialect twitter corpus for gender age and language variety identification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "twitter user geolocation using deep multiview learning"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "recurrent neural network for text classification with multi task learning"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "short text language identification for under resourced languages"):
  with members:
    - "improving short text classification through global augmentation methods"
    - "towards neural machine translation for african languages"
    - "what s in a domain learning domain robust text representations using adversarial training"
    - "the wili benchmark dataset for written language identification"
    - "improved text language identification for the south african languages"
    - "hierarchical character word models for language identification"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "experiments in cuneiform language identification"):
  with members:
    - "moroco the moldavian and romanian dialectal corpus"
    - "language and dialect identification of cuneiform texts"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploring the use of text classification in the legal domain"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-5 (induced by source paper "language identification on massive datasets of short message using an attention mechanism cnn"):
  with members:
    - "ncrf an open source neural sequence labeling toolkit"
    - "lanidenn multilingual language identification on character window"
    - "hierarchical character word models for language identification"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "language model adaptation for language and dialect identification of text"):
  with members:
    - "automatic language identification system for hindi and magahi"
    - "automatic identification of closely related indian languages resources and experiments"
    - "german dialect identification using classifier ensembles"
    - "discriminating between indo aryan languages using svm ensembles"

Connected through hyperedge HE-7 (induced by source paper "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"):
  with members:
    - "convolutional neural networks and language embeddings for end to end dialect recognition"
    - "automated essay scoring with string kernels and word embeddings"
    - "can string kernels pass the test of time in native language identification"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-8 (induced by source paper "classifier ensembles for dialect and language variety identification"):
  with members:
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "discriminating between indo aryan languages using svm ensembles"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "automatic dialect detection in arabic broadcast speech"

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)