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: "towards playlist generation algorithms using rnns trained on within track transitions"
  Abstract: We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.

Connected through hyperedge HE-1 (induced by source paper "representation exploration and recommendation of music playlists"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "deep content user embedding model for music recommendation"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "variational neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-2 (induced by source paper "deep layered learning in mir"):
  with members:
    - "polyphonic pitch tracking with deep layered learning"
    - "onsets and frames dual objective piano transcription"
    - "deep residual learning for image recognition"
    - "learning fine grained features via a cnn tree for large scale classification"
    - "the application of two level attention models in deep convolutional neural network for fine grained image classification"
    - "the state of the art ten years after a state of the art future research in music information retrieval"
    - "attention for fine grained categorization"
    - "knowledge matters importance of prior information for optimization"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for music classification and regression tasks"):
  with members:
    - "the effects of noisy labels on deep convolutional neural networks for music tagging"
    - "look listen and learn"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "explaining deep convolutional neural networks on music classification"
    - "automatic tagging using deep convolutional neural networks"
    - "soundnet learning sound representations from unlabeled video"
    - "theano a python framework for fast computation of mathematical expressions"

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.AI(Artificial Intelligence)