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: "character composition model with convolutional neural networks for dependency parsing on morphologically rich languages"
  Abstract: We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et al., 2015) by a margin of 3% on average.

Connected through hyperedge HE-1 (induced by source paper "exploiting synchronized lyrics and vocal features for music emotion detection"):
  with members:
    - "convolutional attention networks for multimodal emotion recognition from speech and text data"
    - "end to end music source separation is it possible in the waveform domain"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "a tutorial on deep learning for music information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "phonetic and semantic embedding of spoken words with applications in spoken content retrieval"):
  with members:
    - "segmental audio word2vec representing utterances as sequences of vectors with applications in spoken term detection"
    - "an iterative closest point method for unsupervised word translation"
    - "non adversarial unsupervised word translation"
    - "towards unsupervised automatic speech recognition trained by unaligned speech and text only"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "unsupervised adaptation with domain separation networks for robust speech recognition"
    - "unsupervised learning of semantic audio representations"
    - "end to end neural segmental models for speech recognition"

Connected through hyperedge HE-3 (induced by source paper "speech2vec a sequence to sequence framework for learning word embeddings from speech"):
  with members:
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "learning word like units from joint audio visual analysis"
    - "supervised and unsupervised transfer learning for question answering"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "speech based visual question answering"
    - "learning word embeddings from speech"
    - "towards machine comprehension of spoken content initial toefl listening comprehension test by machine"

Connected through hyperedge HE-4 (induced by source paper "a general purpose tagger with convolutional neural networks"):
  with members:
    - "convolutional sequence to sequence learning"
    - "character based neural machine translation"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "boosting named entity recognition with neural character embeddings"
    - "a universal part of speech tagset"

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)