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: "the music streaming sessions dataset"
  Abstract: At the core of many important machine learning problems faced by online streaming services is a need to model how users interact with the content. These problems can often be reduced to a combination of 1) sequentially recommending items to the user, and 2) exploiting the user's interactions with the items as feedback for the machine learning model. Unfortunately, there are no public datasets currently available that enable researchers to explore this topic. In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of approximately 150 million listening sessions and associated user actions. Furthermore, we provide audio features and metadata for the approximately 3.7 million unique tracks referred to in the logs. This is the largest collection of such track metadata currently available to the public. This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

Connected through hyperedge HE-1 (induced by source paper "sequential modeling of sessions using recurrent neural networks for skip prediction"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "what makes imagenet good for transfer learning"
    - "session based recommendations with recurrent neural networks"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "session based sequential skip prediction via recurrent neural networks"):
  with members:
    - "field aware factorization machines in a real world online advertising system"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "a neural representation of sketch drawings"
    - "wide deep learning for recommender systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "e commerce in your inbox product recommendations at scale"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "sequential skip prediction with few shot in streamed music contents"):
  with members:
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "learning to compare relation network for few shot learning"
    - "a simple neural attentive meta learner"
    - "instance normalization the missing ingredient for fast stylization"
    - "language modeling with gated convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "modelling sequential music track skips using a multi rnn approach"):
  with members:
    - "lstm benchmarks for deep learning frameworks"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"
    - "session based recommendations with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "skip prediction using boosting trees based on acoustic features of tracks in sessions"):
  with members:
    - "automatic playlist continuation using a hybrid recommender system combining features from text and audio"
    - "current challenges and visions in music recommender systems research"

Connected through hyperedge HE-6 (induced by source paper "the potential of the confluence of theoretical and algorithmic modeling in music recommendation"):
  with members:
    - "online music listening culture of kids and adolescents listening analysis and music recommendation tailored to the young"

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)