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: "syntactic and semantic features for code switching factored language models"
  Abstract: This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.

Connected through hyperedge HE-1 (induced by source paper "a survey on neural network language models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character word lstm language models"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "theanolm an extensible toolkit for neural network language modeling"
    - "gated word character recurrent language model"
    - "character level language modeling with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "code switched language models using neural based synthetic data from parallel sentences"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "code switching language modeling using syntax aware multi task learning"
    - "learning comment generation by leveraging user generated data"
    - "get to the point summarization with pointer generator networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a dataset for building code mixed goal oriented conversation systems"):
  with members:
    - "part of speech tagging for code switched data"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a persona based neural conversation model"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-4 (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-5 (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"
    - "challenges of computational processing of code switching"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "code switching language modeling using syntax aware multi task learning"):
  with members:
    - "a multi task approach for named entity recognition in social media data"
    - "recurrent neural network grammars"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "using the output embedding to improve language models"
    - "multi task sequence to sequence learning"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "code switched language models using dual rnns and same source pretraining"):
  with members:
    - "dual language models for code mixed speech recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "challenges of computational processing of code switching"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging for code mixed indian social media text at icon 2015"
    - "one model two languages training bilingual parsers with harmonized treebanks"
    - "a universal part of speech tagset"

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)