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: "neural multi source morphological reinflection"
  Abstract: We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pairs of source form and source tag for a lemma. The motivation is that it is beneficial to have access to more than one source form since different source forms can provide complementary information, e.g., different stems. We further present a novel extension to the encoder- decoder recurrent neural architecture, consisting of multiple encoders, to better solve the task. We show that our new architecture outperforms single-source reinflection models and publish our dataset for multi-source morphological reinflection to facilitate future research.

Connected through hyperedge HE-1 (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"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "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 language correction with character based attention"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-2 (induced by source paper "multi space variational encoder decoders for semi supervised labeled sequence transduction"):
  with members:
    - "controlling output length in neural encoder decoders"
    - "single model encoder decoder with explicit morphological representation for 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"

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)