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 machine transliteration preliminary results"
  Abstract: Machine transliteration is the process of automatically transforming the script of a word from a source language to a target language, while preserving pronunciation. Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. In this paper a character-based encoder-decoder model has been proposed that consists of two Recurrent Neural Networks. The encoder is a Bidirectional recurrent neural network that encodes a sequence of symbols into a fixed-length vector representation, and the decoder generates the target sequence using an attention-based recurrent neural network. The encoder, the decoder and the attention mechanism are jointly trained to maximize the conditional probability of a target sequence given a source sequence. Our experiments on different datasets show that the proposed encoder-decoder model is able to achieve significantly higher transliteration quality over traditional statistical models.

Connected through hyperedge HE-1 (induced by source paper "urdu english machine transliteration using neural networks"):
  with members:
    - "how much does tokenization affect neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "six challenges for neural machine translation"
    - "natural language processing state of the art current trends and challenges"
    - "transfer learning for low resource neural machine translation"
    - "polish to english statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "learning to reformulate the queries on the web"):
  with members:
    - "neural information retrieval a literature review"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character aware neural language models"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (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"
    - "sequence to sequence neural network models for transliteration"
    - "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 architectures for named entity recognition"

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)