human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a large scale comparison of historical text normalization systems"):
  Abstract: There is no consensus on the state-of-the-art approach to historical text normalization. Many techniques have been proposed, including rule-based methods, distance metrics, character-based statistical machine translation, and neural encoder--decoder models, but studies have used different datasets, different evaluation methods, and have come to different conclusions. This paper presents the largest study of historical text normalization done so far. We critically survey the existing literature and report experiments on eight languages, comparing systems spanning all categories of proposed normalization techniques, analysing the effect of training data quantity, and using different evaluation methods. The datasets and scripts are made publicly available.

Includes members:
  Member "xnmt the extensible neural machine translation toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation")
  Member "an evaluation of neural machine translation models on historical spelling normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "evaluating historical text normalization systems how well do they generalize":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling")
  Member "marian fast neural machine translation in c":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context in neural machine translation a review of models and evaluations")
  Member "improving historical spelling normalization with bi directional lstms and multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modernizing historical documents a user study")
  Member "an open diachronic corpus of historical spanish annotation criteria and automatic modernisation of spelling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an evaluation of neural machine translation models on historical spelling normalization")

Question: Please tell me which class the source paper 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)