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: "challenges of computational processing of code switching"
  Abstract: This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed. It refers to code-switching which has become more popular in our daily life and therefore obtains an increasing amount of attention from the research community. We report our experience that cov- ers not only core NLP tasks such as normalisation, language identification, language modelling, part-of-speech tagging and dependency parsing but also more downstream ones such as machine translation and automatic speech recognition. We highlight and discuss the key problems for each of the tasks with supporting examples from different language pairs and relevant previous work.

Connected through hyperedge HE-1 (induced by source paper "unsupervised sentiment analysis for code mixed data"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "a survey of cross lingual word embedding models"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "learning word vectors for 157 languages"
    - "sentiment analysis of code mixed languages leveraging resource rich languages"
    - "word translation without parallel data"

Connected through hyperedge HE-2 (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"
    - "on the end to end solution to mandarin english code switching speech recognition"
    - "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"
    - "end to end attention based large vocabulary speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "code switching language modeling with bilingual word embeddings a case study for egyptian arabic english"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "graph based bilingual word embedding for statistical machine translation"
    - "theanolm an extensible toolkit for neural network language modeling"
    - "cross lingual models of word embeddings an empirical comparison"
    - "bilingual distributed word representations from document aligned comparable data"
    - "do multi sense embeddings improve natural language understanding"
    - "multilingual models for compositional distributed semantics"

Connected through hyperedge HE-4 (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"
    - "building a unified code switching asr system for south african languages"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "language modeling for code switching evaluation integration of monolingual data and discriminative training"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "large margin neural language model"
    - "on the state of the art of evaluation in neural language models"
    - "intelligible language modeling with input switched affine networks"
    - "regularizing and optimizing lstm language models"
    - "language modeling for code switched data challenges and approaches"
    - "dual language models for code mixed speech recognition"

Connected through hyperedge HE-6 (induced by source paper "code switching sentence generation by generative adversarial networks and its application to data augmentation"):
  with members:
    - "building a unified code switching asr system for south african languages"
    - "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"
    - "syntactic and semantic features for code switching factored language models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "semi supervised acoustic model training for speech with code switching"):
  with members:
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "joining hands exploiting monolingual treebanks for parsing of code mixing data"):
  with members:
    - "shallow parsing pipeline for hindi english code mixed social media text"
    - "many languages one parser"
    - "natural language processing almost from scratch"

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)