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 "stack pointer networks for dependency parsing"):
  Abstract: We introduce a novel architecture for dependency parsing: \emph{stack-pointer networks} (\textbf{\textsc{StackPtr}}). Combining pointer networks~\citep{vinyals2015pointer} with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the status of the depth-first search and the pointer networks select one child for the word at the top of the stack at each step. The \textsc{StackPtr} parser benefits from the information of the whole sentence and all previously derived subtree structures, and removes the left-to-right restriction in classical transition-based parsers. Yet, the number of steps for building any (including non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with $O(n^2)$ time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-the-art performance on 21 of them.

Includes members:
  Member "neural probabilistic model for non projective mst parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "graph convolutional encoders for syntax aware neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "unsupervised ranking model for entity coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inter sentence relation extraction with document level graph convolutional neural network")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "training with exploration improves a greedy stack lstm parser":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "dropout with expectation linear regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parsing as pretraining")

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)