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 "a seq to seq transformer premised temporal convolutional network for chinese word segmentation"):
  Abstract: The prevalent approaches of Chinese word segmentation task almost rely on the Bi-LSTM neural network. However, the methods based the Bi-LSTM have some inherent drawbacks: hard to parallel computing, little efficient in applying the Dropout method to inhibit the Overfitting and little efficient in capturing the character information at the more distant site of a long sentence for the word segmentation task. In this work, we propose a sequence-to-sequence transformer model for Chinese word segmentation, which is premised a type of convolutional neural network named temporal convolutional network. The model uses the temporal convolutional network to construct an encoder, and uses one layer of fully-connected neural network to build a decoder, and applies the Dropout method to inhibit the Overfitting, and captures the character information at the distant site of a sentence by adding the layers of the encoder, and binds Conditional Random Fields model to train parameters, and uses the Viterbi algorithm to infer the final result of the Chinese word segmentation. The experiments on traditional Chinese corpora and simplified Chinese corpora show that the performance of Chinese word segmentation of the model is equivalent to the performance of the methods based the Bi-LSTM, and the model has a tremendous growth in parallel computing than the models based the Bi-LSTM.

Includes members:
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  Member "adversarial multi criteria learning for chinese word segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "neural word segmentation with rich pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "neural word segmentation learning for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")
  Member "a convolutional encoder model for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gret global representation enhanced transformer")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "character aware neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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)