human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "contextual phonetic pretraining for end to end utterance level language and speaker recognition"):
  Abstract: Pretrained contextual word representations in NLP have greatly improved performance on various downstream tasks. For speech, we propose contextual frame representations that capture phonetic information at the acoustic frame level and can be used for utterance-level language, speaker, and speech recognition. These representations come from the frame-wise intermediate representations of an end-to-end, self-attentive ASR model (SAN-CTC) on spoken utterances. We first train the model on the Fisher English corpus with context-independent phoneme labels, then use its representations at inference time as features for task-specific models on the NIST LRE07 closed-set language recognition task and a Fisher speaker recognition task, giving significant improvements over the state-of-the-art on both (e.g., language EER of 4.68% on 3sec utterances, 23% relative reduction in speaker EER). Results remain competitive when using a novel dilated convolutional model for language recognition, or when ASR pretraining is done with character labels only.

Includes members:
  Member "audio linguistic embeddings for spoken sentences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "analyzing deep cnn based utterance embeddings for acoustic model adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "comparison of speech representations for automatic quality estimation in multi speaker text to speech synthesis")
  Member "speaker embedding extraction with phonetic information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two stage training for chinese dialect recognition")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "speech2vec a sequence to sequence framework for learning word embeddings from speech":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "voxceleb2 deep speaker recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "everybody s talkin let me talk as you want")
  Member "analyzing hidden representations in end to end automatic speech recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "untangling in invariant speech recognition")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)