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: "multilingual bottleneck features for subword modeling in zero resource languages"
  Abstract: How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sense to leverage information from large annotated corpora in other languages, for example in the form of multilingual bottleneck features (BNFs) obtained from a supervised speech recognition system. In this work, we evaluate the benefits of BNFs for subword modeling (feature extraction) in six unseen languages on a word discrimination task. First we establish a strong unsupervised baseline by combining two existing methods: vocal tract length normalisation (VTLN) and the correspondence autoencoder (cAE). We then show that BNFs trained on a single language already beat this baseline; including up to 10 languages results in additional improvements which cannot be matched by just adding more data from a single language. Finally, we show that the cAE can improve further on the BNFs if high-quality same-word pairs are available.

Connected through hyperedge HE-1 (induced by source paper "towards zero shot learning for automatic phonemic transcription"):
  with members:
    - "multilingual speech recognition with corpus relatedness sampling"
    - "sequence based multi lingual low resource speech recognition"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-2 (induced by source paper "analyzing asr pretraining for low resource speech to text translation"):
  with members:
    - "fluent translations from disfluent speech in end to end speech translation"
    - "massively multilingual adversarial speech recognition"
    - "tied multitask learning for neural speech translation"
    - "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling"
    - "low resource speech to text translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "sequence based multi lingual low resource speech recognition"

Connected through hyperedge HE-3 (induced by source paper "pre training on high resource speech recognition improves low resource speech to text translation"):
  with members:
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "a case study on using speech to translation alignments for language documentation"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "a very low resource language speech corpus for computational language documentation experiments"
    - "unsupervised pretraining for sequence to sequence learning"
    - "transfer learning for low resource neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "asr free cnn dtw keyword spotting using multilingual bottleneck features for almost zero resource languages"):
  with members:
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "learning acoustic word embeddings with temporal context for query by example speech search"
    - "fast asr free and almost zero resource keyword spotting using dtw and cnns for humanitarian monitoring"
    - "end to end asr free keyword search from speech"
    - "very deep convolutional networks for end to end speech recognition"
    - "discriminative acoustic word embeddings recurrent neural network based approaches"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"

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)