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 "two local models for neural constituent parsing"):
  Abstract: Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-art statistical parsers. With the rise of deep learning, however, it has been shown that local output decisions can give highly competitive accuracies, thanks to the power of dense neural input representations that embody global syntactic information. We investigate two conceptually simple local neural models for constituent parsing, which make local decisions to constituent spans and CFG rules, respectively. Consistent with previous findings along the line, our best model gives highly competitive results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1.

Includes members:
  Member "in order transition based constituent parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  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 "a minimal span based neural constituency parser":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ecsp a new task for emotion cause span pair extraction and classification")
  Member "neural probabilistic model for non projective mst parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "improving neural parsing by disentangling model combination and reranking effects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable syntax aware language models using knowledge distillation")
  Member "span based constituency parsing with a structure label system and provably optimal dynamic oracles":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")

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)