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: "deep content user embedding model for music recommendation"
  Abstract: Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained. The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further.

Connected through hyperedge HE-1 (induced by source paper "improving collaborative metric learning with efficient negative sampling"):
  with members:
    - "word2vec applied to recommendation hyperparameters matter"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning spread out local feature descriptors"
    - "how to train triplet networks with 100k identities"
    - "smart mining for deep metric learning"
    - "item2vec neural item embedding for collaborative filtering"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-2 (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"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "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-3 (induced by source paper "enhancing music features by knowledge transfer from user item log data"):
  with members:
    - "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"
    - "transfer learning for music classification and regression tasks"
    - "fma a dataset for music analysis"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "adam a method for stochastic optimization"
    - "fitnets hints for thin deep nets"

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)