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: "hierarchical character word models for language identification"
  Abstract: Social media messages' brevity and unconventional spelling pose a challenge to language identification. We introduce a hierarchical model that learns character and contextualized word-level representations for language identification. Our method performs well against strong base- lines, and can also reveal code-switching.

Connected through hyperedge HE-1 (induced by source paper "short text language identification for under resourced languages"):
  with members:
    - "improving short text classification through global augmentation methods"
    - "automatic language identification in texts a survey"
    - "towards neural machine translation for african languages"
    - "what s in a domain learning domain robust text representations using adversarial training"
    - "the wili benchmark dataset for written language identification"
    - "improved text language identification for the south african languages"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "language identification on massive datasets of short message using an attention mechanism cnn"):
  with members:
    - "automatic language identification in texts a survey"
    - "ncrf an open source neural sequence labeling toolkit"
    - "lanidenn multilingual language identification on character window"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "automated word stress detection in russian"):
  with members:
    - "charagram embedding words and sentences via character n grams"

Connected through hyperedge HE-4 (induced by source paper "a study on dialog act recognition using character level tokenization"):
  with members:
    - "a latent variable recurrent neural network for discourse relation language models"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "character level convolutional networks for text classification"
    - "the influence of context on dialogue act recognition"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"

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)