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 feature enriched neural model for joint chinese word segmentation and part of speech tagging"
  Abstract: Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experimental results on five different datasets show the effectiveness of our proposed model.

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 "attention is all you need for chinese word segmentation"):
  with members:
    - "self attention with relative position representations"
    - "switch lstms for multi criteria chinese word segmentation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "a structured self attentive sentence embedding"
    - "neural word segmentation with rich pretraining"

Connected through hyperedge HE-3 (induced by source paper "a graph based model for joint chinese word segmentation and dependency parsing"):
  with members:
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "searching for effective neural extractive summarization what works and what s next"
    - "pre training with whole word masking for chinese bert"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-4 (induced by source paper "neural semi markov conditional random fields for robust character based part of speech tagging"):
  with members:
    - "wronging a right generating better errors to improve grammatical error detection"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "mimicking word embeddings using subword rnns"
    - "a general purpose tagger with convolutional neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural word segmentation learning for chinese"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

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)