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: "state of the art chinese word segmentation with bi lstms"
  Abstract: A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. #R##N#Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neural-network architectures. #R##N#Furthermore, our error analysis shows that out-of-vocabulary words remain challenging for neural-network models, and many of the remaining errors are unlikely to be fixed through architecture changes. #R##N#Instead, more effort should be made on exploring resources for further improvement.

Connected through hyperedge HE-1 (induced by source paper "rethinking generalization of neural models a named entity recognition case study"):
  with members:
    - "a closer look at data bias in neural extractive summarization models"
    - "transfer learning for sequence labeling using source model and target data"
    - "stiffness a new perspective on generalization in neural networks"
    - "design challenges and misconceptions in neural sequence labeling"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarially robust generalization requires more data"

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"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-3 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "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-4 (induced by source paper "bert meets chinese word segmentation"):
  with members:
    - "chinese word segmentation another decade review 2007 2017"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "convolutional neural network with word embeddings 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"

Connected through hyperedge HE-5 (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"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "convolutional neural network with word embeddings 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-6 (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"
    - "switch lstms for multi criteria chinese word segmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "neural word segmentation learning for chinese"

Connected through hyperedge HE-7 (induced by source paper "hyperbolic deep learning for chinese natural language understanding"):
  with members:
    - "hyperbolic attention networks"
    - "riemannian adaptive optimization methods"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "attention is all you need"
    - "sgdr stochastic gradient descent with restarts"

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)