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 unified deep neural network for speaker and language recognition"
  Abstract: Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks. In this work we show how these gains are possible using a single DNN for both speaker and language recognition. The unified DNN approach is shown to yield substantial performance improvements on the the 2013 Domain Adaptation Challenge speaker recognition task (55% reduction in EER for the out-of-domain condition) and on the NIST 2011 Language Recognition Evaluation (48% reduction in EER for the 30s test condition).

Connected through hyperedge HE-1 (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition"):
  with members:
    - "audio linguistic embeddings for spoken sentences"
    - "analyzing deep cnn based utterance embeddings for acoustic model adaptation"
    - "speaker embedding extraction with phonetic information"
    - "deep contextualized word representations"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "voxceleb2 deep speaker recognition"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "multiclass language identification using deep learning on spectral images of audio signals"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "language identification using deep convolutional recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "end to end attention based text dependent speaker verification"):
  with members:
    - "advances in very deep convolutional neural networks for lvcsr"
    - "theano a python framework for fast computation of mathematical expressions"
    - "end to end text dependent speaker verification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "the ibm 2015 english conversational telephone speech recognition system"
    - "very deep convolutional networks for large scale image recognition"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (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"
    - "the mgb 2 challenge arabic multi dialect broadcast media recognition"
    - "automatic dialect detection in arabic broadcast speech"

Connected through hyperedge HE-5 (induced by source paper "recursive whitening transformation for speaker recognition on language mismatched condition"):
  with members:
    - "ku ispl speaker recognition systems under language mismatch condition for nist 2016 speaker recognition evaluation"

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)