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: "dual language models for code mixed speech recognition"
  Abstract: In this work, we present a new approach to language modeling for bilingual code-switched text. This technique, called dual language models, involves building two complementary monolingual language models and combining them using a probabilistic model for switching between the two. The objective of this technique is to improve generalization when the amount of code-switched training data is limited. We evaluate the efficacy of our approach using a conversational Mandarin-English speech corpus. Using our model, we obtain significant improvements in both perplexity measures and automatic speech recognition error rates compared to a standard bilingual language model.

Connected through hyperedge HE-1 (induced by source paper "training a code switching language model with monolingual data"):
  with members:
    - "acoustically grounded word embeddings for improved acoustics to word speech recognition"
    - "constrained output embeddings for end to end code switching speech recognition with only monolingual data"
    - "learn to code switch data augmentation using copy mechanism on language modeling"
    - "language modeling for code switching evaluation integration of monolingual data and discriminative training"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "direct acoustics to word models for english conversational speech recognition"

Connected through hyperedge HE-2 (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"
    - "challenges of computational processing of code switching"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "code switched language models using dual rnns and same source pretraining"):
  with members:
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "syntactic and semantic features for code switching factored language models"

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)