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 "neural transductive learning and beyond morphological generation in the minimal resource setting"):
  Abstract: Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a partial paradigm, generating all missing forms. We propose two new methods for the minimal-resource setting: (i) Paradigm transduction: Since we assume only few paradigms available for training, neural seq2seq models are able to capture relationships between paradigm cells, but are tied to the idiosyncracies of the training set. Paradigm transduction mitigates this problem by exploiting the input subset of inflected forms at test time. (ii) Source selection with high precision (SHIP): Multi-source models which learn to automatically select one or multiple sources to predict a target inflection do not perform well in the minimal-resource setting. SHIP is an alternative to identify a reliable source if training data is limited. On a 52-language benchmark dataset, we outperform the previous state of the art by up to 9.71% absolute accuracy.

Includes members:
  Member "unimorph 2 0 universal morphology":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linspector multilingual probing tasks for word representations")
  Member "one shot neural cross lingual transfer for paradigm completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact hard monotonic attention for character level transduction")
  Member "multi space variational encoder decoders for semi supervised labeled sequence transduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational template machine for data to text generation")
  Member "unlabeled data for morphological generation with character based sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal lemmatizer a sequence to sequence model for lemmatizing universal dependencies treebanks")
  Member "modeling target side inflection in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploring benefits of transfer learning in neural machine translation")
  Member "neural multi source morphological reinflection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi space variational encoder decoders for semi supervised labeled sequence transduction")
  Member "neural language correction with character based attention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint contextual modeling for asr correction and language understanding")

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)