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 "constrained output embeddings for end to end code switching speech recognition with only monolingual data"):
  Abstract: The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models. In this work, we propose a method to train an improved end-to-end code-switching ASR using only monolingual data. Our method encourages the distributions of output token embeddings of monolingual languages to be similar, and hence, promotes the ASR model to easily code-switch between languages. Specifically, we propose to use Jensen-Shannon divergence and cosine distance based constraints. The former will enforce output embeddings of monolingual languages to possess similar distributions, while the later simply brings the centroids of two distributions to be close to each other. Experimental results demonstrate high effectiveness of the proposed method, yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English code-switching ASR task.

Includes members:
  Member "learn to code switch data augmentation using copy mechanism on language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training a code switching language model with monolingual data")
  Member "espnet end to end speech processing toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")
  Member "towards end to end code switching speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analyzing phonetic and graphemic representations in end to end automatic speech recognition")
  Member "acoustic and textual data augmentation for improved asr of code switching speech":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition")
  Member "on the end to end solution to mandarin english code switching speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rnn transducer with language bias for end to end mandarin english code switching speech recognition")
  Member "semi supervised acoustic model training for speech with code switching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi graph decoding for code switching asr")
  Member "code switching sentence generation by generative adversarial networks and its application to data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring bias in gan based data augmentation for small samples")
  Member "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end automatic speech recognition integrated with ctc based voice activity detection")

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)