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: "improving recurrent neural networks for sequence labelling"
  Abstract: In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.

Connected through hyperedge HE-1 (induced by source paper "hybrid neural models for sequence modelling the best of three worlds"):
  with members:
    - "seq2biseq bidirectional output wise recurrent neural networks for sequence modelling"
    - "universal transformers"
    - "deep contextualized word representations"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "label dependencies aware recurrent neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "label dependencies aware recurrent neural networks"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-3 (induced by source paper "effective spoken language labeling with deep recurrent neural networks"):
  with members:
    - "label dependencies aware recurrent neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "efficient estimation of word representations in vector space"

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)