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 "knptc knowledge and neural machine translation powered chinese pinyin typo correction"):
  Abstract: Chinese pinyin input methods are very important for Chinese language processing. Actually, users may make typos inevitably when they input pinyin. Moreover, pinyin typo correction has become an increasingly important task with the popularity of smartphones and the mobile Internet. How to exploit the knowledge of users typing behaviors and support the typo correction for acronym pinyin remains a challenging problem. To tackle these challenges, we propose KNPTC, a novel approach based on neural machine translation (NMT). In contrast to previous work, KNPTC is able to integrate explicit knowledge into NMT for pinyin typo correction, and is able to learn to correct a variety of typos without the guidance of manually selected constraints or languagespecific features. In this approach, we first obtain the transition probabilities between adjacent letters based on large-scale real-life datasets. Then, we construct the "ground-truth" alignments of training sentence pairs by utilizing these probabilities. Furthermore, these alignments are integrated into NMT to capture sensible pinyin typo correction patterns. KNPTC is applied to correct typos in real-life datasets, which achieves 32.77% increment on average in accuracy rate of typo correction compared against the state-of-the-art system.

Includes members:
  Member "a nested attention neural hybrid model for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting")
  Member "neural language correction with character based attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint contextual modeling for asr correction and language understanding")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention over parameters for dialogue systems")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)