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: "an exploration of neural sequence to sequence architectures for automatic post editing"
  Abstract: In this work, we explore multiple neural architectures adapted for the task of automatic post-editing of machine translation output. We focus on neural end-to-end models that combine both inputs $mt$ (raw MT output) and $src$ (source language input) in a single neural architecture, modeling $\{mt, src\} \rightarrow pe$ directly. Apart from that, we investigate the influence of hard-attention models which seem to be well-suited for monolingual tasks, as well as combinations of both ideas. We report results on data sets provided during the WMT-2016 shared task on automatic post-editing and can demonstrate that dual-attention models that incorporate all available data in the APE scenario in a single model improve on the best shared task system and on all other published results after the shared task. Dual-attention models that are combined with hard attention remain competitive despite applying fewer changes to the input.

Connected through hyperedge HE-1 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "think again networks and the delta loss"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "direct output connection for a high rank language model"
    - "trellis networks for sequence modeling"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "visual story post editing"):
  with members:
    - "on how users edit computer generated visual stories"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "glac net glocal attention cascading networks for multi image cued story generation"
    - "attention is all you need"
    - "attention strategies for multi source sequence to sequence learning"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "asynchronous bidirectional decoding for neural machine translation"):
  with members:
    - "neural system combination for machine translation"
    - "deep neural machine translation with linear associative unit"
    - "towards decoding as continuous optimization in neural machine translation"
    - "twin networks matching the future for sequence generation"
    - "memory enhanced decoder for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-5 (induced by source paper "retrieve and refine improved sequence generation models for dialogue"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating sentences by editing prototypes"
    - "quickedit editing text translations by crossing words out"
    - "adapting sequence models for sentence correction"
    - "controllable abstractive summarization"
    - "search engine guided non parametric neural machine translation"
    - "iterative refinement for machine translation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-6 (induced by source paper "escape a large scale synthetic corpus for automatic post editing"):
  with members:
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-7 (induced by source paper "lig cristal system for the wmt17 automatic post editing task"):
  with members:
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "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)