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: "single model encoder decoder with explicit morphological representation for reinflection"
  Abstract: Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag. We propose a new way of modeling this task with neural encoder-decoder models. Our approach reduces the amount of required training data for this architecture and achieves state-of-the-art results, making encoder-decoder models applicable to morphological reinflection even for low-resource languages. We further present a new automatic correction method for the outputs based on edit trees.

Connected through hyperedge HE-1 (induced by source paper "the conll sigmorphon 2018 shared task universal morphological reinflection"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "marrying universal dependencies and universal morphology"
    - "unsupervised disambiguation of syncretism in inflected lexicons"
    - "finding the way from a to a sub character morphological inflection for the sigmorphon 2018 shared task"
    - "copenhagen at conll sigmorphon 2018 multilingual inflection in context with explicit morphosyntactic decoding"
    - "a structured variational autoencoder for contextual morphological inflection"
    - "attention is all you need"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"

Connected through hyperedge HE-2 (induced by source paper "on the complexity and typology of inflectional morphological systems"):
  with members:
    - "paradigm completion for derivational morphology"
    - "opennmt open source toolkit for neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "one shot neural cross lingual transfer for paradigm completion"):
  with members:
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "cross lingual morphological tagging for low resource languages"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "morphological inflection generation using character sequence to sequence learning"
    - "multi task sequence to sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "multi space variational encoder decoders for semi supervised labeled sequence transduction"):
  with members:
    - "controlling output length in neural encoder decoders"
    - "neural multi source morphological reinflection"
    - "variational neural machine translation"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "language as a latent variable discrete generative models for sentence compression"
    - "semantic parsing with semi supervised sequential autoencoders"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-5 (induced by source paper "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "learning to transduce with unbounded memory"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "context aware prediction of derivational word forms"):
  with members:
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "transition based dependency parsing with stack long short term memory"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "nonsymbolic text representation"):
  with members:
    - "character based neural machine translation"
    - "online segment to segment neural transduction"
    - "a joint model for word embedding and word morphology"
    - "gated word character recurrent language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "sharing network parameters for crosslingual named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "morphological inflection generation with hard monotonic attention"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "online segment to segment neural transduction"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "neural machine translation in linear time"
    - "factored neural machine translation"
    - "a neural transducer"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

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)