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 "incremental parsing with minimal features using bi directional lstm"):
  Abstract: Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese.

Includes members:
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parsing as pretraining")
  Member "transition based dependency parsing with stack long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "structured training for neural network transition based parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concurrent parsing of constituency and dependency")
  Member "grammar as a foreign language":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parsing as pretraining")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "improving neural networks by preventing co adaptation of feature detectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)