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 "synchronous speech recognition and speech to text translation with interactive decoding"):
  Abstract: Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years. Compared to the traditional pipeline system, the end-to-end ST model has potential benefits of lower latency, smaller model size, and less error propagation. However, it is notoriously difficult to implement such a model without transcriptions as intermediate. Existing works generally apply multi-task learning to improve translation quality by jointly training end-to-end ST along with automatic speech recognition (ASR). However, different tasks in this method cannot utilize information from each other, which limits the improvement. Other works propose a two-stage model where the second model can use the hidden state from the first one, but its cascade manner greatly affects the efficiency of training and inference process. In this paper, we propose a novel interactive attention mechanism which enables ASR and ST to perform synchronously and interactively in a single model. Specifically, the generation of transcriptions and translations not only relies on its previous outputs but also the outputs predicted in the other task. Experiments on TED speech translation corpora have shown that our proposed model can outperform strong baselines on the quality of speech translation and achieve better speech recognition performances as well.

Includes members:
  Member "synchronous bidirectional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  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 "end to end speech translation with knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multilingual end to end speech translation")
  Member "synchronous bidirectional inference for neural sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "tied multitask learning for neural speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "controllable time delay transformer for real time punctuation prediction and disfluency detection")
  Member "end to end automatic speech translation of audiobooks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-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)