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 "a novel neural network model for joint pos tagging and graph based dependency parsing"):
  Abstract: We present a novel neural network model that learns POS tagging and graph-based dependency  parsing  jointly.  Our  model uses bidirectional LSTMs to learn feature representations  shared  for  both  POS  tagging  and  dependency  parsing  tasks,  thus handling the feature-engineering problem.#R##N#Our  extensive  experiments,   on  19  languages   from   the   Universal   Dependencies project, show that our model outperforms the state-of-the-art neural network-based  Stack-propagation  model  for  joint#R##N#POS  tagging  and  transition-based  dependency  parsing,  resulting  in  a  new  state of  the  art.#R##N#Our  code  is  open-source and  available  together  with  pre-trained#R##N#models  at: https://github.com/ datquocnguyen/jPTDP#R##N#.

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 "deep multitask learning for semantic dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "an empirical study for vietnamese dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a neural joint model for vietnamese word segmentation pos tagging and dependency parsing")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a")
  Member "bi directional attention with agreement for dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "improved transition based parsing by modeling characters instead of words with lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transition based dependency parsing using perceptron learner")
  Member "transition based dependency parsing with stack long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving entity linking by modeling latent entity type information")

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)