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: "an evaluation of neural machine translation models on historical spelling normalization"
  Abstract: In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models for low-resource languages. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "scheduled multi task learning from syntax to translation"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "multilevel text normalization with sequence to sequence networks and multisource learning"):
  with members:
    - "evaluating historical text normalization systems how well do they generalize"
    - "deep contextualized word representations"
    - "joint semantic synthesis and morphological analysis of the derived word"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "improving historical spelling normalization with bi directional lstms and multi task learning"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-4 (induced by source paper "improving lemmatization of non standard languages with joint learning"):
  with members:
    - "deep contextualized word representations"
    - "universal language model fine tuning for text classification"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "a large scale comparison of historical text normalization systems"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "evaluating historical text normalization systems how well do they generalize"
    - "marian fast neural machine translation in c"
    - "improving historical spelling normalization with bi directional lstms and multi task learning"
    - "an open diachronic corpus of historical spanish annotation criteria and automatic modernisation of spelling"

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)