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: "improving historical spelling normalization with bi directional lstms and multi task learning"
  Abstract: Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model's performance further.

Connected through hyperedge HE-1 (induced by source paper "modernizing historical documents a user study"):
  with members:
    - "a call for clarity in reporting bleu scores"
    - "opennmt open source toolkit for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "multilevel text normalization with sequence to sequence networks and multisource learning"):
  with members:
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "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"
    - "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-3 (induced by source paper "an interactive machine translation framework for modernizing historical documents"):
  with members:
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "a call for clarity in reporting bleu scores"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "a large scale comparison of historical text normalization systems"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "marian fast neural machine translation in c"
    - "an open diachronic corpus of historical spanish annotation criteria and automatic modernisation of spelling"

Connected through hyperedge HE-5 (induced by source paper "identifying beneficial task relations for multi task learning in deep neural networks"):
  with members:
    - "how transferable are neural networks in nlp applications"
    - "improving sentence compression by learning to predict gaze"
    - "keystroke dynamics as signal for shallow syntactic parsing"
    - "multi task sequence to sequence learning"
    - "a universal part of speech tagset"
    - "a model of inductive bias learning"

Connected through hyperedge HE-6 (induced by source paper "multi task learning of keyphrase boundary classification"):
  with members:
    - "online segment to segment neural transduction"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "multi task sequence to sequence learning"
    - "natural language processing almost from scratch"
    - "a model of inductive bias learning"

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)