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 "exact hard monotonic attention for character level transduction"):
  Abstract: Many common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic models, outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias in these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns alignment jointly. With the help of dynamic programming, we are able to compute the exact marginalization over all alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks.

Includes members:
  Member "imitation learning for neural morphological string transduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards realistic practices in low resource natural language processing the development set")
  Member "hard non monotonic attention for character level transduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse sequence to sequence models")
  Member "align and copy uzh at sigmorphon 2017 shared task for morphological reinflection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural transductive learning and beyond morphological generation in the minimal resource setting")
  Member "multi space variational encoder decoders for semi supervised labeled sequence transduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational template machine for data to text generation")
  Member "online segment to segment neural transduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs")
  Member "sequence to sequence neural network models for transliteration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arabic natural language processing an overview")
  Member "morphological inflection generation with hard monotonic attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel iterative edit models for local sequence transduction")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)