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 "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  Abstract: Previously, a machine speech chain, which is based on sequence-to-sequence deep learning, was proposed to mimic speech perception and production behavior. Such chains separately processed listening and speaking by automatic speech recognition (ASR) and text-to-speech synthesis (TTS) and simultaneously enabled them to teach each other in semi-supervised learning when they received unpaired data. Unfortunately, this speech chain study is limited to speech and textual modalities. In fact, natural communication is actually multimodal and involves both auditory and visual sensory systems. Although the said speech chain reduces the requirement of having a full amount of paired data, in this case we still need a large amount of unpaired data. In this research, we take a further step and construct a multimodal chain and design a closely knit chain architecture that combines ASR, TTS, image captioning, and image production models into a single framework. The framework allows the training of each component without requiring a large number of parallel multimodal data. Our experimental results also show that an ASR can be further trained without speech and text data and cross-modal data augmentation remains possible through our proposed chain, which improves the ASR performance.

Includes members:
  Member "deep audio visual speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks")
  Member "towards robust neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "multi scale alignment and contextual history for attention mechanism in sequence to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "speech to speech translation between untranscribed unknown languages")
  Member "machine speech chain with one shot speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech to speech translation between untranscribed unknown languages")
  Member "turbo learning for captionbot and drawingbot":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate grounded image captions without localization supervision")
  Member "end to end feedback loss in speech chain framework via straight through estimator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "english conversational telephone speech recognition by humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition")
  Member "the microsoft 2017 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep segmental phonetic posterior grams based discovery of non categories in l2 english speech")

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)