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: "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
  Abstract: We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.

Connected through hyperedge HE-1 (induced by source paper "fgn fusion glyph network for chinese named entity recognition"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "attention is all you need"
    - "glyph aware embedding of chinese characters"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "dice loss for data imbalanced nlp tasks"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "libra r cnn towards balanced learning for object detection"
    - "glyce glyph vectors for chinese character representations"
    - "towards accurate one stage object detection with ap loss"
    - "dsreg using distant supervision as a regularizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "chinese ner using lattice lstm"

Connected through hyperedge HE-4 (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"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "decoupled weight decay regularization"
    - "joint pos tagging and dependency parsing with transition based neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi criteria chinese word segmentation with transformer"):
  with members:
    - "effective neural solution for multi criteria word segmentation"
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "state of the art chinese word segmentation with bi lstms"
    - "switch lstms for multi criteria chinese word segmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-6 (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"
    - "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"
    - "robsut wrod reocginiton via semi character recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "multiple character embeddings for chinese word segmentation"):
  with members:
    - "adversarial multi criteria learning for chinese word segmentation"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "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-8 (induced by source paper "shoma at parseme shared task on automatic identification of vmwes neural multiword expression tagging with high generalisation"):
  with members:
    - "fine tuned language models for text classification"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)