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: "ensembling factored neural machine translation models for automatic post editing and quality estimation"
  Abstract: This work presents a novel approach to jointly tackling Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems. Word-level features which have proven effective for QE are included as input factors, expanding the representation of the original source and the machine translation hypothesis, which are used to generate an automatically post-edited hypothesis. We train a suite of NMT models which use different input representations, but share the same output space. These models are then ensembled together, and tuned for both the APE and the QE task. We thus attempt to connect the state-of-the-art approaches to APE and QE within a single framework. Our models achieve state-of-the-art results in both tasks, with the only difference in the tuning step which learns weights for each component of the ensemble.

Connected through hyperedge HE-1 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "convolutional sequence to sequence learning"
    - "pre translation for neural machine translation"
    - "systran s pure neural machine translation systems"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"

Connected through hyperedge HE-2 (induced by source paper "low resource machine translation using interlinear glosses"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "character aware decoder for translation into morphologically rich languages"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "improving character based decoding using target side morphological information for neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "polyglot neural language models a case study in cross lingual phonetic representation learning"

Connected through hyperedge HE-3 (induced by source paper "escape a large scale synthetic corpus for automatic post editing"):
  with members:
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-4 (induced by source paper "lig cristal system for the wmt17 automatic post editing task"):
  with members:
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "multi source neural translation"
    - "multi task sequence to sequence learning"
    - "neural machine translation by jointly learning to align and translate"
    - "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)