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: "a unified tagging solution bidirectional lstm recurrent neural network with word embedding"
  Abstract: Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN for a unified tagging solution that can be applied to various tagging tasks including part-of-speech tagging, chunking and named entity recognition. Instead of exploiting specific features carefully optimized for each task, our solution only uses one set of task-independent features and internal representations learnt from unlabeled text for all tasks.Requiring no task specific knowledge or sophisticated feature engineering, our approach gets nearly state-of-the-art performance in all these three tagging tasks.

Connected through hyperedge HE-1 (induced by source paper "choosing the right word using bidirectional lstm tagger for writing support systems"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "fake news detection on social media a data mining perspective"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "paraphrase generation with deep reinforcement learning"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-2 (induced by source paper "toponym identification in epidemiology articles a deep learning approach"):
  with members:
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "going wider recurrent neural network with parallel cells"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "character aware neural language models"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "generating sequences with recurrent neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "semantic tagging with deep residual networks"):
  with members:
    - "byte based language identification with deep convolutional networks"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "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"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-5 (induced by source paper "byte based language identification with deep convolutional networks"):
  with members:
    - "semantic tagging with deep residual networks"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "discriminating similar languages evaluations and explorations"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "bi directional lstm recurrent neural network for chinese word segmentation"):
  with members:
    - "lstm a search space odyssey"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "very deep convolutional networks for large scale image recognition"
    - "how to construct deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

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)