human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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#.

Connected through hyperedge HE-1 (induced by source paper "an augmented transformer architecture for natural language generation tasks"):
  with members:
    - "self attention with relative position representations"
    - "allennlp a deep semantic natural language processing platform"
    - "end to end dense video captioning with masked transformer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "recurrent additive networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "from pos tagging to dependency parsing for biomedical event extraction"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "deep biaffine attention for neural dependency parsing"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"):
  with members:
    - "relation extraction a survey"
    - "deep residual learning for weakly supervised relation extraction"
    - "attention is all you need"
    - "attending to all mention pairs for full abstract biological relation extraction"
    - "incorporating relation paths in neural relation extraction"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "quasi recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "an improved neural network model for joint"):
  with members:
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "neural probabilistic model for non projective mst parsing"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"

Connected through hyperedge HE-5 (induced by source paper "towards jointud part of speech tagging and lemmatization using recurrent neural networks"):
  with members:
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "regularizing and optimizing lstm language models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "end to end memory networks"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-6 (induced by source paper "vncorenlp a vietnamese natural language processing toolkit"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "from word segmentation to pos tagging for vietnamese"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "an empirical study for vietnamese dependency parsing"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "amr dependency parsing with a typed semantic algebra"):
  with members:
    - "amr parsing as graph prediction with latent alignment"
    - "universal semantic parsing"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "an incremental parser for abstract meaning representation"
    - "grammar as a foreign language"

Connected through hyperedge HE-8 (induced by source paper "end to end graph based tag parsing with neural networks"):
  with members:
    - "finding function in form compositional character models for open vocabulary word representation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Question: Please tell me which class the center node 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)