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 "constituent parsing as sequence labeling"):
  Abstract: We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "straight to the tree constituency parsing with neural syntactic distance":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "zero shot sequence labeling transferring knowledge from sentences to tokens":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequence labeling parsing by learning across representations")
  Member "ncrf an open source neural sequence labeling toolkit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parsing as pretraining")
  Member "constituency parsing with a self attentive encoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "in order transition based constituent parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "a minimal span based neural constituency parser":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ecsp a new task for emotion cause span pair extraction and classification")
  Member "optimizing spectral learning for parsing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unlexicalized transition based discontinuous constituency parsing")

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)