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: "speech recognition challenge in the wild arabic mgb 3"
  Abstract: This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition in the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the recognition task was based on more than 1,200 hours broadcast TV news recordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic using a multi-genre collection of Egyptian YouTube videos. Seven genres were used for the data collection: comedy, cooking, family/kids, fashion, drama, sports, and science (TEDx). A total of 16 hours of videos, split evenly across the different genres, were divided into adaptation, development and evaluation data sets. The Arabic MGB-Challenge comprised two tasks: A) Speech transcription, evaluated on the MGB-3 test set, along with the 10 hour MGB-2 test set to report progress on the MGB-2 evaluation; B) Arabic dialect identification, introduced this year in order to distinguish between four major Arabic dialects - Egyptian, Levantine, North African, Gulf, as well as Modern Standard Arabic. Two hours of audio per dialect were released for development and a further two hours were used for evaluation. For dialect identification, both lexical features and i-vector bottleneck features were shared with participants in addition to the raw audio recordings. Overall, thirteen teams submitted ten systems to the challenge. We outline the approaches adopted in each system, and summarise the evaluation results.

Connected through hyperedge HE-1 (induced by source paper "moroco the moldavian and romanian dialectal corpus"):
  with members:
    - "improving the results of string kernels in sentiment analysis and arabic dialect identification by adapting them to your test set"
    - "automatic identification of closely related indian languages resources and experiments"
    - "automated essay scoring with string kernels and word embeddings"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "can string kernels pass the test of time in native language identification"
    - "language modeling with gated convolutional networks"
    - "a character level convolutional neural network for distinguishing similar languages and dialects"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "arabic natural language processing an overview"):
  with members:
    - "madari a web interface for joint arabic morphological annotation and spelling correction"
    - "asda analyseur syntaxique du dialecte alg e rien dans un but d analyse s e mantique"
    - "sequence to sequence neural network models for transliteration"
    - "a large scale corpus of gulf arabic"
    - "bag of tricks for efficient text classification"
    - "character aware neural language models"
    - "automatic dialect detection in arabic broadcast speech"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "english broadcast news speech recognition by humans and machines"):
  with members:
    - "the capio 2017 conversational speech recognition system"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "regularizing and optimizing lstm language models"
    - "language modeling with highway lstm"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "improvements to deep convolutional neural networks for lvcsr"

Connected through hyperedge HE-4 (induced by source paper "darts dialectal arabic transcription system"):
  with members:
    - "phonetic and graphemic systems for multi genre broadcast transcription"
    - "wav2letter the fastest open source speech recognition system"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the mgb 2 challenge arabic multi dialect broadcast media recognition"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-5 (induced by source paper "automatic language identification in texts a survey"):
  with members:
    - "n gram and neural language models for discriminating similar languages"
    - "including dialects and language varieties in author profiling"
    - "labeling of query words using conditional random field"
    - "discriminating similar languages evaluations and explorations"
    - "language detection for short text messages in social media"
    - "automatic dialect detection in arabic broadcast speech"
    - "recurrent neural network for language detection on twitter code switching corpus"

Connected through hyperedge HE-6 (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"
    - "automatic language identification in texts a survey"
    - "automated essay scoring with string kernels and word embeddings"
    - "can string kernels pass the test of time in native language identification"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-7 (induced by source paper "classifier ensembles for dialect and language variety identification"):
  with members:
    - "automatic language identification in texts a survey"
    - "unibuckernel reloaded first place in arabic dialect identification for the second year in a row"
    - "discriminating between indo aryan languages using svm ensembles"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-8 (induced by source paper "german dialect identification using classifier ensembles"):
  with members:
    - "automatic language identification in texts a survey"
    - "discriminating between indo aryan languages using svm ensembles"

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)