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: "label dependencies aware recurrent neural networks"
  Abstract: In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as CRFs. In this work we propose a solution far simpler but very effective: an evolution of the simple Jordan RNN, where labels are re-injected as input into the network, and converted into embeddings, in the same way as words. We compare this RNN variant to all the other RNN models, Elman and Jordan RNN, LSTM and GRU, on two well-known tasks of Spoken Language Understanding (SLU). Thanks to label embeddings and their combination at the hidden layer, the proposed variant, which uses more parameters than Elman and Jordan RNNs, but far fewer than LSTM and GRU, is more effective than other RNNs, but also outperforms sophisticated CRF models.

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"
    - "improving recurrent neural networks for sequence labelling"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "a multi layer lstm based approach for robot command interaction modeling"):
  with members:
    - "data recombination for neural semantic parsing"
    - "classifying relations by ranking with convolutional neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "effective spoken language labeling with deep recurrent neural networks"):
  with members:
    - "improving recurrent neural networks for sequence labelling"
    - "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)