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 chinese word segmentation with dictionary knowledge"
  Abstract: Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, and usually cannot utilize the useful information in Chinese dictionary. In this paper, we propose two methods to exploit the dictionary information for CWS. The first one is based on pseudo labeled data generation, and the second one is based on multi-task learning. The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.

Connected through hyperedge HE-1 (induced by source paper "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task domain adaptation for sequence tagging"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "named entity recognition with bidirectional lstm cnns"
    - "boosting named entity recognition with neural character embeddings"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "neural chinese word segmentation with lexicon and unlabeled data via posterior regularization"):
  with members:
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "transfer deep learning for low resource chinese word segmentation with a novel neural network"
    - "multi task domain adaptation for sequence tagging"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "efficient estimation of word representations in vector space"

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)