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: "sequence to sequence neural network models for transliteration"
  Abstract: Transliteration is a key component of machine translation systems and software internationalization. This paper demonstrates that neural sequence-to-sequence models obtain state of the art or close to state of the art results on existing datasets. In an effort to make machine transliteration accessible, we open source a new Arabic to English transliteration dataset and our trained models.

Connected through hyperedge HE-1 (induced by source paper "arabic natural language processing an overview"):
  with members:
    - "madari a web interface for joint arabic morphological annotation and spelling correction"
    - "asda analyseur syntaxique du dialecte alg e rien dans un but d analyse s e mantique"
    - "speech recognition challenge in the wild arabic mgb 3"
    - "a large scale corpus of gulf arabic"
    - "bag of tricks for efficient text classification"
    - "character aware neural language models"
    - "automatic dialect detection in arabic broadcast speech"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (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"
    - "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "online segment to segment neural transduction"
    - "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-3 (induced by source paper "anetac arabic named entity transliteration and classification dataset"):
  with members:
    - "neural machine translation by jointly learning to align and translate"
    - "japanese english cross language information retrieval exploration of query translation and transliteration"

Connected through hyperedge HE-4 (induced by source paper "phonology augmented statistical framework for machine transliteration using limited linguistic resources"):
  with members:
    - "rule based transliteration scheme for english to punjabi"
    - "japanese english cross language information retrieval exploration of query translation and transliteration"

Connected through hyperedge HE-5 (induced by source paper "efficient sequence labeling with actor critic training"):
  with members:
    - "multimodal named entity recognition for short social media posts"
    - "opennmt open source toolkit for neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "asynchronous methods for deep reinforcement learning"
    - "an actor critic algorithm for sequence prediction"
    - "neural machine transliteration preliminary results"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "hard non monotonic attention for character level transduction"):
  with members:
    - "efficient softmax approximation for gpus"
    - "morphological inflection generation with hard monotonic attention"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "effective approaches to attention based neural machine translation"
    - "strategies for training large vocabulary neural language models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "design challenges in named entity transliteration"):
  with members:
    - "attention is all you need"
    - "character based neural machine translation"
    - "sequence to sequence neural net models for grapheme to phoneme conversion"
    - "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)