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: "domain robust feature extraction for rapid low resource asr development"
  Abstract: Developing a practical speech recognizer for a low resource language is challenging, not only because of the (potentially unknown) properties of the language, but also because test data may not be from the same domain as the available training data. In this paper, we focus on the latter challenge, i.e. domain mismatch, for systems trained using a sequence-based criterion. We demonstrate the effectiveness of using a pre-trained English recognizer, which is robust to such mismatched conditions, as a domain normalizing feature extractor on a low resource language. In our example, we use Turkish Conversational Speech and Broadcast News data. This enables rapid development of speech recognizers for new languages which can easily adapt to any domain. Testing in various cross-domain scenarios, we achieve relative improvements of around 25% in phoneme error rate, with improvements being around 50% for some domains.

Connected through hyperedge HE-1 (induced by source paper "exploring phoneme level speech representations for end to end speech translation"):
  with members:
    - "revisiting character based neural machine translation with capacity and compression"
    - "optimizing segmentation granularity for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "low resource speech to text translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "self attentional acoustic models"

Connected through hyperedge HE-2 (induced by source paper "phoneme level language models for sequence based low resource asr"):
  with members:
    - "sequence based multi lingual low resource speech recognition"
    - "comparison of decoding strategies for ctc acoustic models"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"
    - "enriching word vectors with subword information"
    - "character level incremental speech recognition with recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"

Connected through hyperedge HE-3 (induced by source paper "multilingual speech recognition with corpus relatedness sampling"):
  with members:
    - "sequence based multi lingual low resource speech recognition"
    - "enriching word vectors with subword information"
    - "a curriculum learning method for improved noise robustness in automatic speech recognition"
    - "achieving human parity in conversational speech recognition"
    - "character aware neural language models"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "thchs 30 a free chinese speech corpus"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "the ariel cmu systems for lorehlt18"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "sequence based multi lingual low resource speech recognition"
    - "enriching word vectors with subword information"
    - "learning crosslingual word embeddings without bilingual corpora"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "bucking the trend large scale cost focused active learning for statistical machine translation"
    - "neural machine translation by jointly learning to align and translate"

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)