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 characterwise windowed approach to hebrew morphological segmentation"):
  Abstract: This paper presents a novel approach to the segmentation of orthographic word forms in contemporary Hebrew, focusing purely on splitting without carrying out morphological analysis or disambiguation. Casting the analysis task as character-wise binary classification and using adjacent character and word-based lexicon-lookup features, this approach achieves over 98% accuracy on the benchmark SPMRL shared task data for Hebrew, and 97% accuracy on a new out of domain Wikipedia dataset, an improvement of ~4% and 5% over previous state of the art performance.

Includes members:
  Member "universal word segmentation implementation and interpretation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "char rnn and active learning for hashtag segmentation")
  Member "neural word segmentation learning for chinese":
    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 "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "polyglot ner massive multilingual named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entity projection via machine translation for cross lingual ner")
  Member "a universal part of speech tagset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual alignment of contextual word representations")
  Member "unsupervised discovery of morphemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parsimonious morpheme segmentation with an application to enriching word embeddings")

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)