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 "dependency grammar induction with a neural variational transition based parser"):
  Abstract: Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-of-the-art results on this task but they require $O(n^3)$ run time. Transition-based models enable faster inference with $O(n)$ time complexity, but their performance still lags behind. In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with $O(n)$ time complexity. We train the parser with an integration of variational inference, posterior regularization and variance reduction techniques. The resulting framework outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to graph-based models, both on the English Penn Treebank and on the Universal Dependency Treebank. In an empirical comparison, we show that our approach substantially increases parsing speed over graph-based models.

Includes members:
  Member "global transition based non projective dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the non utility of structural features in bilstm based dependency parsers")
  Member "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parsing as pretraining")
  Member "crf autoencoder for unsupervised dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "a generative parser with a discriminative recognition algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised neural semantic parsing with a generative ranker")
  Member "recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parsing as pretraining")
  Member "language as a latent variable discrete generative models for sentence compression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a probabilistic formulation of unsupervised text style transfer")

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)