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 "few shot and zero shot learning for historical text normalization"):
  Abstract: Historical text normalization often relies on small training datasets. Recent work has shown that multi-task learning can sometimes lead to significant improvements by exploiting synergies with related datasets, but there has been no systematic study of multi-task learning strategies across different datasets from different languages. This paper evaluates 63 multi-task learning strategies for sequence-to-sequence-based historical text normalization across ten datasets from eight languages, using autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary tasks. We observe consistent, significant improvements across languages when training data for the target task is limited, but minimal or no improvements when training data is abundant. Finally, we show that zero-shot learning outperforms the simple, but relatively strong, identity baseline.

Includes members:
  Member "a large scale comparison of historical text normalization systems":
    also connected through other hyperedges:
      (none)
  Member "tied multitask learning for neural speech translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "xnmt the extensible neural machine translation toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling")
  Member "unimorph 2 0 universal morphology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linspector multilingual probing tasks for word representations")
  Member "scheduled multi task learning from syntax to translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "one shot neural cross lingual transfer for paradigm completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")

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)