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: "transfer deep learning for low resource chinese word segmentation with a novel neural network"
  Abstract: Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. We propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets.

Connected through hyperedge HE-1 (induced by source paper "a new clustering neural network for chinese word segmentation"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "pkuseg a toolkit for multi domain chinese word segmentation"
    - "graph neural networks a review of methods and applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "bidirectional lstm crf models for sequence tagging"
    - "deep residual learning for image recognition"

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"
    - "neural chinese word segmentation with dictionary knowledge"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "multi task domain adaptation for sequence tagging"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "pkuseg a toolkit for multi domain chinese word segmentation"):
  with members:
    - "exploring segment representations for neural segmentation models"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-4 (induced by source paper "improving social media text summarization by learning sentence weight distribution"):
  with members:
    - "label embedding network learning label representation for soft training of deep networks"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "lcsts a large scale chinese short text summarization dataset"
    - "abstractive multi document summarization via phrase selection and merging"

Connected through hyperedge HE-5 (induced by source paper "deep stacking networks for low resource chinese word segmentation with transfer learning"):
  with members:
    - "deep multi task representation learning a tensor factorisation approach"
    - "neural word segmentation learning for chinese"
    - "adam a method for stochastic optimization"
    - "learning with augmented features for heterogeneous domain adaptation"
    - "natural language processing almost from scratch"
    - "learning from multiple outlooks"

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)