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 "analyzing asr pretraining for low resource speech to text translation"):
  Abstract: Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pretraining an end-to-end model on automatic speech recognition (ASR) data from a high-resource language. However, it is not clear what factors --e.g., language relatedness or size of the pretraining data-- yield the biggest improvements, or whether pretraining can be effectively combined with other methods such as data augmentation. Here, we experiment with pretraining on datasets of varying sizes, including languages related and unrelated to the AST source language. We find that the best predictor of final AST performance is the word error rate of the pretrained ASR model, and that differences in ASR/AST performance correlate with how phonetic information is encoded in the later RNN layers of our model. We also show that pretraining and data augmentation yield complementary benefits for AST.

Includes members:
  Member "fluent translations from disfluent speech in end to end speech translation":
    also connected through other hyperedges:
      (none)
  Member "massively multilingual adversarial speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual acoustic word embedding models for processing zero resource languages")
  Member "tied multitask learning for neural speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "multilingual sequence to sequence speech recognition architecture transfer learning and language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")
  Member "end to end automatic speech translation of audiobooks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "pre training on high resource speech recognition improves low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual end to end speech translation")
  Member "sequence based multi lingual low resource speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards zero shot learning for automatic phonemic transcription")

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)