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: "a deep multimodal approach for cold start music recommendation"
  Abstract: An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.

Connected through hyperedge HE-1 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"):
  with members:
    - "signed distance based deep memory recommender"
    - "a hybrid approach to music playlist continuation based on playlist song membership"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "adversarial personalized ranking for recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-2 (induced by source paper "seer an explainable deep learning midi based hybrid song recommender system"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "machine learning approaches to hybrid music recommender systems"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "neural collaborative filtering"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-3 (induced by source paper "artist and style exposure bias in collaborative filtering based music recommendations"):
  with members:
    - "beyond personalization research directions in multistakeholder recommendation"

Connected through hyperedge HE-4 (induced by source paper "deep content user embedding model for music recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "current challenges and visions in music recommender systems research"
    - "representation learning of music using artist labels"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "deep heterogeneous autoencoders for collaborative filtering"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "collaborative deep learning for recommender systems"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "toward faultless content based playlists generation for instrumentals"):
  with members:
    - "understanding data augmentation for classification when to warp"
    - "deep learning and music adversaries"
    - "the state of the art ten years after a state of the art future research in music information retrieval"

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)