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: "current challenges and visions in music recommender systems research"
  Abstract: Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. #R##N#The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

Connected through hyperedge HE-1 (induced by source paper "towards evaluating user profiling methods based on explicit ratings on item features"):
  with members:
    - "session based hotel recommendations challenges and future directions"
    - "deep learning based recommender system a survey and new perspectives"
    - "combining aspects of genetic algorithms with weighted recommender hybridization"

Connected through hyperedge HE-2 (induced by source paper "the unfairness of popularity bias in music recommendation a reproducibility study"):
  with members:
    - "the unfairness of popularity bias in recommendation"
    - "global and country specific mainstreaminess measures definitions analysis and usage for improving personalized music recommendation systems"

Connected through hyperedge HE-3 (induced by source paper "skip prediction using boosting trees based on acoustic features of tracks in sessions"):
  with members:
    - "the music streaming sessions dataset"
    - "automatic playlist continuation using a hybrid recommender system combining features from text and audio"

Connected through hyperedge HE-4 (induced by source paper "global and country specific mainstreaminess measures definitions analysis and usage for improving personalized music recommendation systems"):
  with members:
    - "online music listening culture of kids and adolescents listening analysis and music recommendation tailored to the young"

Connected through hyperedge HE-5 (induced by source paper "evaluating recommender system algorithms for generating local music playlists"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "an analysis of approaches taken in the acm recsys challenge 2018 for automatic music playlist continuation"):
  with members:
    - "automatic playlist continuation using a hybrid recommender system combining features from text and audio"
    - "a line in the sand recommendation or ad hoc retrieval"
    - "neural collaborative filtering"
    - "xgboost a scalable tree boosting system"
    - "enriching word vectors with subword information"
    - "metadata embeddings for user and item cold start recommendations"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (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"
    - "a deep multimodal approach for cold start music recommendation"
    - "representation learning of music using artist labels"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "local music event recommendation with long tail artists"):
  with members:
    - "probabilistic latent semantic analysis"

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)