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: "hard non monotonic attention for character level transduction"
  Abstract: Character-level string-to-string transduction is an important component of various NLP tasks. The goal is to map an input string to an output string, where the strings may be of different lengths and have characters taken from different alphabets. Recent approaches have used sequence-to-sequence models with an attention mechanism to learn which parts of the input string the model should focus on during the generation of the output string. Both soft attention and hard monotonic attention have been used, but hard non-monotonic attention has only been used in other sequence modeling tasks such as image captioning and has required a stochastic approximation to compute the gradient. In this work, we introduce an exact, polynomial-time algorithm for marginalizing over the exponential number of non-monotonic alignments between two strings, showing that hard attention models can be viewed as neural reparameterizations of the classical IBM Model 1. We compare soft and hard non-monotonic attention experimentally and find that the exact algorithm significantly improves performance over the stochastic approximation and outperforms soft attention.

Connected through hyperedge HE-1 (induced by source paper "sparse sequence to sequence models"):
  with members:
    - "attention is not explanation"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "sparse and constrained attention for neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-2 (induced by source paper "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"):
  with members:
    - "gender bias in contextualized word embeddings"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "biased embeddings from wild data measuring understanding and removing"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

Connected through hyperedge HE-3 (induced by source paper "exact hard monotonic attention for character level transduction"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "online segment to segment neural transduction"
    - "sequence to sequence neural network models for transliteration"
    - "morphological inflection generation with hard monotonic attention"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the sigmorphon 2019 shared task morphological analysis in context and cross lingual transfer for inflection"):
  with members:
    - "exact hard monotonic attention for character level transduction"
    - "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"
    - "transfer learning for low resource neural machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a simple joint model for improved contextual neural lemmatization"):
  with members:
    - "neural factor graph models for cross lingual morphological tagging"
    - "marrying universal dependencies and universal morphology"
    - "show attend and tell neural image caption generation with visual attention"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep 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)