human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural chinese word segmentation with lexicon and unlabeled data via posterior regularization"):
  Abstract: Chinese word segmentation (CWS) is very important for Chinese text processing. Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.

Includes members:
  Member "prior knowledge integration for neural machine translation using posterior regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "neural chinese word segmentation with dictionary knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation")
  Member "adversarial multi criteria learning for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")
  Member "fast and accurate neural word segmentation for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "neural word segmentation with rich pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "transfer deep learning for low resource chinese word segmentation with a novel neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new clustering neural network for chinese word segmentation")
  Member "multi task domain adaptation for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")
  Member "equilibrated adaptive learning rates for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")

Question: Please tell me which class the source paper 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)