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: "code switched language models using dual rnns and same source pretraining"
  Abstract: This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.

Connected through hyperedge HE-1 (induced by source paper "a deep generative model for code switched text"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "code switching language modeling using syntax aware multi task learning"
    - "adversarial evaluation of dialogue models"
    - "generating sentences from a continuous space"
    - "character aware neural language models"
    - "a large annotated corpus for learning natural language inference"
    - "attention based models for speech recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-2 (induced by source paper "analysis of automatic annotation suggestions for hard discourse level tasks in expert domains"):
  with members:
    - "challenges in the automatic analysis of students diagnostic reasoning"
    - "the dalphi annotation framework how its pre annotations can improve annotator efficiency"
    - "learning word vectors for 157 languages"
    - "bayesian ensembles of crowds and deep learners for sequence tagging"
    - "a bayesian approach for sequence tagging with crowds"
    - "end to end incremental learning"
    - "progressive neural networks"
    - "neural end to end learning for computational argumentation mining"

Connected through hyperedge HE-3 (induced by source paper "code switched language models using neural based synthetic data from parallel sentences"):
  with members:
    - "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"
    - "syntactic and semantic features for code switching factored language 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:
    - "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"
    - "challenges of computational processing of code switching"

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"
    - "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)