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 "char rnn and active learning for hashtag segmentation"):
  Abstract: We explore the abilities of character recurrent neural network (char-RNN) for hashtag segmentation. Our approach to the task is the following: we generate synthetic training dataset according to frequent n-grams that satisfy predefined morpho-syntactic patterns to avoid any manual annotation. The active learning strategy limits the training dataset and selects informative training subset. The approach does not require any language-specific settings and is compared for two languages, which differ in inflection degree.

Includes members:
  Member "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "an end to end neural natural language interface for databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building an application independent natural language interface")
  Member "universal word segmentation implementation and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "segmentation free compositional n gram embedding")
  Member "deep active learning for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "a gap based framework for chinese word segmentation via very deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiple character embeddings for chinese word segmentation")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fast and accurate neural word segmentation for chinese":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text")

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)