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: "lyrics based music genre classification using a hierarchical attention network"
  Abstract: Music genre classification, especially using lyrics alone, remains a challenging topic in Music Information Retrieval. In this study we apply recurrent neural network models to classify a large dataset of intact song lyrics. As lyrics exhibit a hierarchical layer structure - in which words combine to form lines, lines form segments, and segments form a complete song - we adapt a hierarchical attention network (HAN) to exploit these layers and in addition learn the importance of the words, lines, and segments. We test the model over a 117-genre dataset and a reduced 20-genre dataset. Experimental results show that the HAN outperforms both non-neural models and simpler neural models, whilst also classifying over a higher number of genres than previous research. Through the learning process we can also visualise which words or lines in a song the model believes are important to classifying the genre. As a result the HAN provides insights, from a computational perspective, into lyrical structure and language features that differentiate musical genres.

Connected through hyperedge HE-1 (induced by source paper "brazilian lyrics based music genre classification using a blstm network"):
  with members:
    - "portuguese word embeddings evaluating on word analogies and natural language tasks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "beyond statistical relations integrating knowledge relations into style correlations for multi label music style classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "review driven multi label music style classification by exploiting style correlations"
    - "semi supervised classification with graph convolutional networks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "lukthung classification using neural networks on lyrics and audios"):
  with members:
    - "end to end learning for music audio tagging at scale"
    - "a deep bag of features model for music auto tagging"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "content based feature exploration for transparent music recommendation using self attentive genre classification"):
  with members:
    - "a structured self attentive sentence embedding"
    - "cnn architectures for large scale audio classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "deep cross modal correlation learning for audio and lyrics in music retrieval"):
  with members:
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "cnn architectures for large scale audio classification"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "multi view recurrent neural acoustic word embeddings"
    - "automatic tagging using deep convolutional neural networks"
    - "deep cross modal hashing"

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.IR(Information Retrieval)