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 "attention passing models for robust and data efficient end to end speech translation"):
  Abstract: Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech. However, experiments are inconclusive on whether the cascade or the direct model is stronger, and have only been conducted under the unrealistic assumption that both are trained on equal amounts of data, ignoring other available speech recognition and machine translation corpora. #R##N#In this paper, we demonstrate that direct speech translation models require more data to perform well than cascaded models, and while they allow including auxiliary data through multi-task training, they are poor at exploiting such data, putting them at a severe disadvantage. As a remedy, we propose the use of end-to-end trainable models with two attention mechanisms, the first establishing source speech to source text alignments, the second modeling source to target text alignment. We show that such models naturally decompose into multi-task-trainable recognition and translation tasks and propose an attention-passing technique that alleviates error propagation issues in a previous formulation of a model with two attention stages. Our proposed model outperforms all examined baselines and is able to exploit auxiliary training data much more effectively than direct attentional models.

Includes members:
  Member "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  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 "low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")
  Member "pre training on high resource speech recognition improves low resource speech to text translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual end to end speech translation")
  Member "structured based curriculum learning for end to end english japanese speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one to many multilingual end to end speech translation")
  Member "sequence to sequence models can directly translate foreign speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "improving lexical choice in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "normalization of input output shared embeddings in text generation models")
  Member "exploiting linguistic resources for neural machine translation using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to multi task learn for better neural machine 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)