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 "representation learning of music using artist album and track information"):
  Abstract: Supervised music representation learning has been performed mainly using semantic labels such as music genres. However, annotating music with semantic labels requires time and cost. In this work, we investigate the use of factual metadata such as artist, album, and track information, which are naturally annotated to songs, for supervised music representation learning. The results show that each of the metadata has individual concept characteristics, and using them jointly improves overall performance.

Includes members:
  Member "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning")
  Member "transfer learning for music classification and regression tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward interpretable music tagging with self attention")
  Member "representation learning of music using artist labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zero shot learning for audio based music classification and tagging")
  Member "fma a dataset for music analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "deep learning and music adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks")

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.IR(Information Retrieval)