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: "theanolm an extensible toolkit for neural network language modeling"
  Abstract: We present a new tool for training neural network language models (NNLMs), scoring sentences, and generating text. The tool has been written using Python library Theano, which allows researcher to easily extend it and tune any aspect of the training process. Regardless of the flexibility, Theano is able to generate extremely fast native code that can utilize a GPU or multiple CPU cores in order to parallelize the heavy numerical computations. The tool has been evaluated in difficult Finnish and English conversational speech recognition tasks, and significant improvement was obtained over our best back-off n-gram models. The results that we obtained in the Finnish task were compared to those from existing RNNLM and RWTHLM toolkits, and found to be as good or better, while training times were an order of magnitude shorter.

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"
    - "gated word character recurrent language model"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "coherent dialogue with attention based language models"

Connected through hyperedge HE-2 (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"
    - "cross lingual models of word embeddings an empirical comparison"
    - "challenges of computational processing of code switching"
    - "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-3 (induced by source paper "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"):
  with members:
    - "character based neural machine translation"
    - "neural versus phrase based machine translation quality a case study"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"
    - "edinburgh neural machine translation systems for wmt 16"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"
    - "character aware neural language models"

Connected through hyperedge HE-4 (induced by source paper "automatic speech recognition with very large conversational finnish and estonian vocabularies"):
  with members:
    - "efficient softmax approximation for gpus"
    - "strategies for training large vocabulary neural language models"
    - "training very deep networks"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "unsupervised discovery of morphemes"

Connected through hyperedge HE-5 (induced by source paper "a character word compositional neural language model for finnish"):
  with members:
    - "fully character level neural machine translation without explicit segmentation"
    - "exploring the limits of language modeling"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "adam a method for stochastic optimization"
    - "compositional morphology for word representations and language modelling"
    - "classes for fast maximum entropy training"

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)