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: "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
  Abstract: This paper presents the submissions by the University of Zurich to the SIGMORPHON 2017 shared task on morphological reinflection. The task is to predict the inflected form given a lemma and a set of morpho-syntactic features. We focus on neural network approaches that can tackle the task in a limited-resource setting. As the transduction of the lemma into the inflected form is dominated by copying over lemma characters, we propose two recurrent neural network architectures with hard monotonic attention that are strong at copying and, yet, substantially different in how they achieve this. The first approach is an encoder-decoder model with a copy mechanism. The second approach is a neural state-transition system over a set of explicit edit actions, including a designated COPY action. We experiment with character alignment and find that naive, greedy alignment consistently produces strong results for some languages. Our best system combination is the overall winner of the SIGMORPHON 2017 Shared Task 1 without external resources. At a setting with 100 training samples, both our approaches, as ensembles of models, outperform the next best competitor.

Connected through hyperedge HE-1 (induced by source paper "exact hard monotonic attention for character level transduction"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "hard non monotonic attention for character level transduction"
    - "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-2 (induced by source paper "neural transductive learning and beyond morphological generation in the minimal resource setting"):
  with members:
    - "unimorph 2 0 universal morphology"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "unlabeled data for morphological generation with character based sequence to sequence models"
    - "modeling target side inflection in neural machine translation"
    - "neural multi source morphological reinflection"
    - "neural language correction with character based attention"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-3 (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"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "string transduction with target language models and insertion handling"):
  with members:
    - "sequence to sequence learning with 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)