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: "evaluating historical text normalization systems how well do they generalize"
  Abstract: We highlight several issues in the evaluation of historical text normalization systems that make it hard to tell how well these systems would actually work in practice---i.e., for new datasets or languages; in comparison to more na\"ive systems; or as a preprocessing step for downstream NLP tools. We illustrate these issues and exemplify our proposed evaluation practices by comparing two neural models against a na\"ive baseline system. We show that the neural models generalize well to unseen words in tests on five languages; nevertheless, they provide no clear benefit over the na\"ive baseline for downstream POS tagging of an English historical collection. We conclude that future work should include more rigorous evaluation, including both intrinsic and extrinsic measures where possible.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "using similarity measures to select pretraining data for ner"
    - "unsupervised data augmentation for consistency training"
    - "deep contextualized word representations"

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"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "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:
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "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 "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "imitation learning for neural morphological string transduction"):
  with members:
    - "classical structured prediction losses for sequence to sequence learning"
    - "searnn training rnns with global local losses"
    - "online segment to segment neural transduction"
    - "sequence to sequence learning as beam search optimization"
    - "an actor critic algorithm for sequence prediction"
    - "training with exploration improves a greedy stack lstm parser"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"

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)