human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learn to code switch data augmentation using copy mechanism on language modeling"
  Abstract: Building large-scale datasets for training code-switching language models is challenging and very expensive. To alleviate this problem using parallel corpus has been a major workaround. However, existing solutions use linguistic constraints which may not capture the real data distribution. In this work, we propose a novel method for learning how to generate code-switching sentences from parallel corpora. Our model uses a Seq2Seq model in combination with pointer networks to align and choose words from the monolingual sentences and form a grammatical code-switching sentence. In our experiment, we show that by training a language model using the augmented sentences we improve the perplexity score by 10% compared to the LSTM baseline.

Connected through hyperedge HE-1 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "towards end to end code switching speech recognition"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "on the end to end solution to mandarin english code switching speech recognition"
    - "semi supervised acoustic model training for speech with code switching"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "advances in joint ctc attention based end to end speech recognition with a deep cnn encoder and rnn lm"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "training a code switching language model with monolingual data"):
  with members:
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "constrained output embeddings for end to end code switching speech recognition with only monolingual data"
    - "language modeling for code switching evaluation integration of monolingual data and discriminative training"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "direct acoustics to word models for english conversational speech recognition"
    - "dual language models for code mixed speech recognition"

Question: Please tell me which class the center node 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)