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: "bi directional lstm recurrent neural network for chinese word segmentation"
  Abstract: Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.

Connected through hyperedge HE-1 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bag of words as target for neural machine translation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-2 (induced by source paper "multiple character embeddings for chinese word segmentation"):
  with members:
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-3 (induced by source paper "switch lstms for multi criteria chinese word segmentation"):
  with members:
    - "adversarial multi criteria learning for chinese word segmentation"
    - "neural word segmentation learning for chinese"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "dag based long short term memory for neural word segmentation"):
  with members:
    - "exploring segment representations for neural segmentation models"
    - "neural word segmentation learning for chinese"
    - "improved semantic representations from tree structured long short term memory networks"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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.LG(Machine Learning)