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 "discontinuous constituent parsing with pointer networks"):
  Abstract: One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin.

Includes members:
  Member "discontinuous constituency parsing with a stack free transition system and a dynamic oracle":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "why attention analyze bilstm deficiency and its remedies in the case of ner")
  Member "left to right dependency parsing with pointer networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequence labeling parsing by learning across representations")
  Member "unlexicalized transition based discontinuous constituency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discontinuous constituency parsing with a stack free transition system and a dynamic oracle")
  Member "stack pointer networks for dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sg net syntax guided machine reading comprehension")
  Member "simpler but more accurate semantic dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient sentence embedding via semantic subspace analysis")
  Member "recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "deep biaffine attention for neural dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")

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)