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 "neural semi markov conditional random fields for robust character based part of speech tagging"):
  Abstract: Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. But these models still rely on correct token boundaries. In this paper, we propose a novel end-to-end character-level model and demonstrate its effectiveness in multilingual settings and when token boundaries are noisy. Our model is a semi-Markov conditional random field with neural networks for character and segment representation. It requires no tokenizer. The model matches state-of-the-art baselines for various languages and significantly outperforms them on a noisy English version of a part-of-speech tagging benchmark dataset. Our code and the noisy dataset are publicly available at this http URL.

Includes members:
  Member "wronging a right generating better errors to improve grammatical error detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system")
  Member "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "mimicking word embeddings using subword rnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "a general purpose tagger with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving neural sequence labelling using additional linguistic information")
  Member "empower sequence labeling with task aware neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keeping it simple implementation and performance of the proto principle of adaptation and learning in the language sciences")
  Member "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "neural word segmentation learning for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "authorship attribution in bangla literature using character level cnn")

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)