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: "building a unified code switching asr system for south african languages"
  Abstract: We present our first efforts towards building a single multilingual automatic speech recognition (ASR) system that can process code-switching (CS) speech in five languages spoken within the same population. This contrasts with related prior work which focuses on the recognition of CS speech in bilingual scenarios. Recently, we have compiled a small five-language corpus of South African soap opera speech which contains examples of CS between 5 languages occurring in various contexts such as using English as the matrix language and switching to other indigenous languages. The ASR system presented in this work is trained on 4 corpora containing English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho CS speech. The interpolation of multiple language models trained on these language pairs enables the ASR system to hypothesize mixed word sequences from these 5 languages. We evaluate various state-of-the-art acoustic models trained on this 5-lingual training data and report ASR accuracy and language recognition performance on the development and test sets of the South African multilingual soap opera corpus.

Connected through hyperedge HE-1 (induced by source paper "end to end code switching asr for low resourced language pairs"):
  with members:
    - "multi graph decoding for code switching asr"
    - "a survey of code switched speech and language processing"
    - "advancing acoustic to word ctc model"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "towards language universal end to end speech recognition"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"

Connected through hyperedge HE-2 (induced by source paper "a survey of code switched speech and language processing"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "hindi english code switching speech corpus"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "a dataset for building code mixed goal oriented conversation systems"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "crowdsourcing universal part of speech tags for code switching"
    - "a multi task approach for named entity recognition in social media data"

Connected through hyperedge HE-3 (induced by source paper "codeswitch reddit exploration of written multilingual discourse in online discussion forums"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "multilingual constituency parsing with self attention and pre training"
    - "native language cognate effects on second language lexical choice"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "challenges of computational processing of code switching"

Connected through hyperedge HE-4 (induced by source paper "semi supervised acoustic model training for five lingual code switched asr"):
  with members:
    - "on the end to end solution to mandarin english code switching speech recognition"

Connected through hyperedge HE-5 (induced by source paper "code switching sentence generation by generative adversarial networks and its application to data augmentation"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "dual language models for code mixed speech recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "challenges of computational processing of code switching"
    - "syntactic and semantic features for code switching factored language models"
    - "adam a method for stochastic optimization"

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)