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: "exact hard monotonic attention for character level transduction"
  Abstract: Many common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic models, outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias in these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. With the help of dynamic programming, we are able to compute the exact marginalization over all alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks.

Connected through hyperedge HE-1 (induced by source paper "pushing the limits of low resource morphological inflection"):
  with members:
    - "automatic keyboard layout design for low resource latin script languages"
    - "rapid adaptation of neural machine translation to new languages"
    - "leveraging translations for speech transcription in low resource settings"
    - "unsupervised machine translation using monolingual corpora only"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "attention is all you need"
    - "conll sigmorphon 2017 shared task universal morphological reinflection in 52 languages"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-2 (induced by source paper "the sigmorphon 2019 shared task morphological analysis in context and cross lingual transfer for inflection"):
  with members:
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "neural factor graph models for cross lingual morphological tagging"
    - "marrying universal dependencies and universal morphology"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rapid adaptation of neural machine translation to new languages"
    - "hard non monotonic attention for character level transduction"
    - "transfer learning for low resource neural machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "morphological irregularity correlates with frequency"):
  with members:
    - "on the complexity and typology of inflectional morphological systems"
    - "unimorph 2 0 universal morphology"
    - "unsupervised disambiguation of syncretism in inflected lexicons"

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)