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: "on the end to end solution to mandarin english code switching speech recognition"
  Abstract: Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances. In this work, we study end-to-end (E2E) approaches to the Mandarin-English code-switching speech recognition (CSSR) task. We first examine the effectiveness of using data augmentation and byte-pair encoding (BPE) subword units. More importantly, we propose a multitask learning recipe, where a language identification task is explicitly learned in addition to the E2E speech recognition task. Furthermore, we introduce an efficient word vocabulary expansion method for language modeling to alleviate data sparsity issues under the code-switching scenario. Experimental results on the SEAME data, a Mandarin-English CS corpus, demonstrate the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "rnn transducer with language bias for end to end mandarin english code switching speech recognition"):
  with members:
    - "joint speech recognition and speaker diarization via sequence transduction"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "state of the art speech recognition with sequence to sequence models"
    - "streaming small footprint keyword spotting using sequence to sequence models"
    - "challenges of computational processing of code switching"
    - "end to end attention based large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "constrained output embeddings for end to end code switching speech recognition with only monolingual data"):
  with members:
    - "learn to code switch data augmentation using copy mechanism on language modeling"
    - "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"
    - "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-3 (induced by source paper "semi supervised acoustic model training for five lingual code switched asr"):
  with members:
    - "building a unified code switching asr system for south african languages"

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)