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 "harnessing indirect training data for end to end automatic speech translation tricks of the trade"):
  Abstract: For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models that transcribe with automatic speech recognition (ASR), then translate with machine translation (MT). A major cause of the performance gap is that, while existing AST corpora are small, massive datasets exist for both the ASR and MT subsystems. In this work, we evaluate several data augmentation and pretraining approaches for AST, by comparing all on the same datasets. Simple data augmentation by translating ASR transcripts proves most effective on the English--French augmented LibriSpeech dataset, closing the performance gap from 8.2 to 1.4 BLEU, compared to a very strong cascade that could directly utilize copious ASR and MT data. The same end-to-end approach plus fine-tuning closes the gap on the English--Romanian MuST-C dataset from 6.7 to 3.7 BLEU. In addition to these results, we present practical recommendations for augmentation and pretraining approaches. Finally, we decrease the performance gap to 0.01 BLEU using a Transformer-based architecture.

Includes members:
  Member "transformers with convolutional context for asr":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition")
  Member "attention passing models for robust and data efficient end to end speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one to many multilingual end to end speech translation")
  Member "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "back translation style data augmentation for end to end asr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "espnet end to end speech processing toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "end to end automatic speech translation of audiobooks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "multilingual end to end speech translation")
  Member "leveraging weakly supervised data to improve end to end speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual end to end speech translation")

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)