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 "training a code switching language model with monolingual data"):
  Abstract: A lack of code-switching data complicates the training of code-switching (CS) language models. We propose an approach to train such CS language models on monolingual data only. By constraining and normalizing the output projection matrix in RNN-based language models, we bring embeddings of different languages closer to each other. Numerical and visualization results show that the proposed approaches remarkably improve the performance of CS language models trained on monolingual data. The proposed approaches are comparable or even better than training CS language models with artificially generated CS data. We additionally use unsupervised bilingual word translation to analyze whether semantically equivalent words in different languages are mapped together.

Includes members:
  Member "acoustically grounded word embeddings for improved acoustics to word speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effectiveness of self supervised pre training for speech recognition")
  Member "constrained output embeddings for end to end code switching speech recognition with only monolingual data":
    also connected through other hyperedges:
      (none)
  Member "learn to code switch data augmentation using copy mechanism on language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data")
  Member "language modeling for code switching evaluation integration of monolingual data and discriminative training":
    also connected through other hyperedges:
      (none)
  Member "acoustic and textual data augmentation for improved asr of code switching speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition")
  Member "code switching sentence generation by generative adversarial networks and its application to data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploring bias in gan based data augmentation for small samples")
  Member "direct acoustics to word models for english conversational speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "acoustic to word models with conversational context information")
  Member "dual language models for code mixed speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "language modeling for code switching evaluation integration of monolingual data and discriminative training")

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)