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: "the mgb 2 challenge arabic multi dialect broadcast media recognition"
  Abstract: This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year's English MGB Challenge, which focused on recognition of diverse TV genres, this year, the challenge has an emphasis on handling the diversity in dialect in Arabic speech. Audio data comes from 19 distinct programmes from the Aljazeera Arabic TV channel between March 2005 and December 2015. Programmes are split into three groups: conversations, interviews, and reports. A total of 1,200 hours have been released with lightly supervised transcriptions for the acoustic modelling. For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera.net for a 10 year duration 2000-2011. Two lexicons have been provided, one phoneme based and one grapheme based. Finally, two tasks were proposed for this year's challenge: standard speech transcription, and word alignment. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.

Connected through hyperedge HE-1 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition"):
  with members:
    - "towards end to end code switching speech recognition"
    - "analyzing learned representations of a deep asr performance prediction model"
    - "stream attention based multi array end to end speech recognition"
    - "phonetic and graphemic systems for multi genre broadcast transcription"
    - "analysis methods in neural language processing a survey"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "learning word like units from joint audio visual analysis"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"

Connected through hyperedge HE-2 (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"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "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-3 (induced by source paper "mit qcri arabic dialect identification system for the 2017 multi genre broadcast challenge"):
  with members:
    - "recursive whitening transformation for speaker recognition on language mismatched condition"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "autoencoder based domain adaptation for speaker recognition under insufficient channel information"
    - "ku ispl speaker recognition systems under language mismatch condition for nist 2016 speaker recognition evaluation"
    - "a unified deep neural network for speaker and language recognition"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-4 (induced by source paper "werd using social text spelling variants for evaluating dialectal speech recognition"):
  with members:
    - "english conversational telephone speech recognition by humans and machines"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "enriching word vectors with subword information"
    - "rnn approaches to text normalization a challenge"

Connected through hyperedge HE-5 (induced by source paper "speech recognition challenge in the wild arabic mgb 3"):
  with members:
    - "mit qcri arabic dialect identification system for the 2017 multi genre broadcast challenge"
    - "werd using social text spelling variants for evaluating dialectal speech recognition"
    - "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)