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: "convolutional neural network with word embeddings for chinese word segmentation"
  Abstract: Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heavily rely on manually designed bigram feature, i.e. they are not good at capturing n-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich n-gram features without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineering, the model obtains competitive performance -- 95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves state-of-the-art performance on both datasets -- 96.5% on PKU and 98.0% on MSR, without using any external labeled resource.

Connected through hyperedge HE-1 (induced by source paper "can ner convolutional attention network for chinese named entity recognition"):
  with members:
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "chinese ner using lattice lstm"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial learning for chinese ner from crowd annotations"
    - "combining discrete and neural features for sequence labeling"

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"
    - "fast and accurate neural word segmentation for chinese"
    - "a structured self attentive sentence embedding"
    - "neural word segmentation with rich pretraining"
    - "dag based long short term memory for neural word segmentation"

Connected through hyperedge HE-3 (induced by source paper "bert meets chinese word segmentation"):
  with members:
    - "chinese word segmentation another decade review 2007 2017"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "exploring segment representations for neural segmentation models"

Connected through hyperedge HE-4 (induced by source paper "robust chinese word segmentation with contextualized word representations"):
  with members:
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "exploring segment representations for neural segmentation models"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "effective neural solution for multi criteria word segmentation"):
  with members:
    - "adversarial multi criteria learning for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"
    - "neural architectures for named entity recognition"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (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"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "bi directional lstm recurrent neural network for chinese word segmentation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-7 (induced by source paper "subword encoding in lattice lstm for chinese word segmentation"):
  with members:
    - "chinese ner using lattice lstm"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation with rich pretraining"
    - "neural lattice to sequence models for uncertain inputs"
    - "dag based long short term memory for neural word segmentation"
    - "neural word segmentation learning for chinese"
    - "lattice based recurrent neural network encoders for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised neural word segmentation for chinese via segmental language modeling"):
  with members:
    - "learning word vectors for 157 languages"
    - "online segment to segment neural transduction"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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)