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 general purpose tagger with convolutional neural networks"
  Abstract: We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging. The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.

Connected through hyperedge HE-1 (induced by source paper "improving neural sequence labelling using additional linguistic information"):
  with members:
    - "semi supervised multitask learning for sequence labeling"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "neural architectures for named entity recognition"
    - "linguistic input features improve neural machine translation"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "neural semi markov conditional random fields for robust character based part of speech tagging"):
  with members:
    - "wronging a right generating better errors to improve grammatical error detection"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "mimicking word embeddings using subword rnns"
    - "empower sequence labeling with task aware neural language model"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural word segmentation learning for chinese"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "robsut wrod reocginiton via semi character recurrent neural network"

Connected through hyperedge HE-3 (induced by source paper "modeling composite labels for neural morphological tagging"):
  with members:
    - "neural factor graph models for cross lingual morphological tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "globally normalized transition based neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural architectures for named entity recognition"
    - "attending to characters in neural sequence labeling models"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-4 (induced by source paper "neural morphological tagging for estonian"):
  with members:
    - "modeling composite labels for neural morphological tagging"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural architectures for named entity recognition"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "effective approaches to attention based neural machine translation"
    - "named entity recognition with bidirectional lstm cnns"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "disfluency detection using auto correlational neural networks"):
  with members:
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "parsing speech a neural approach to integrating lexical and acoustic prosodic information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "adam a method for stochastic optimization"

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)