human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "one deep music representation to rule them all a comparative analysis of different representation learning strategies"):
  Abstract: Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new, yet unseen learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning task, the validity of the above hypothesis is questionable for an arbitrary new learning task. In this paper we present the results of our investigation of what the best ways are to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning tasks, as well as multiple deep learning architectures with varying levels of information sharing between tasks, in order to learn music representations. We then validate these representations considering multiple unseen learning tasks for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.

Includes members:
  Member "a comparison on audio signal preprocessing methods for deep neural networks on music tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "music auto tagging using cnns and mel spectrograms with reduced frequency and time resolution")
  Member "identifying beneficial task relations for multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bam born again multi task networks for natural language understanding")
  Member "look listen and learn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "sample level deep convolutional neural networks for music auto tagging using raw waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for audio signal processing")
  Member "transfer learning for music classification and regression tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward interpretable music tagging with self attention")
  Member "one model to learn them all":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention over parameters for dialogue systems")
  Member "deep convolutional neural networks for predominant instrument recognition in polyphonic music":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an attention mechanism for musical instrument recognition")
  Member "fma a dataset for music analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")

Question: Please tell me which class the source paper 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.NE(Neural and Evolutionary Computing)