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 "learning a joint embedding space of monophonic and mixed music signals for singing voice"):
  Abstract: Previous approaches in singer identification have used one of monophonic vocal tracks or mixed tracks containing multiple instruments, leaving a semantic gap between these two domains of audio. In this paper, we present a system to learn a joint embedding space of monophonic and mixed tracks for singing voice. We use a metric learning method, which ensures that tracks from both domains of the same singer are mapped closer to each other than those of different singers. We train the system on a large synthetic dataset generated by music mashup to reflect real-world music recordings. Our approach opens up new possibilities for cross-domain tasks, e.g., given a monophonic track of a singer as a query, retrieving mixed tracks sung by the same singer from the database. Also, it requires no additional vocal enhancement steps such as source separation. We show the effectiveness of our system for singer identification and query-by-singer in both the same-domain and cross-domain tasks.

Includes members:
  Member "revisiting singing voice detection a quantitative review and the future outlook":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "score and lyrics free singing voice generation")
  Member "disambiguating music artists at scale with audio metric learning":
    also connected through other hyperedges:
      (none)
  Member "a hybrid of deep audio feature and i vector for artist recognition":
    also connected through other hyperedges:
      (none)
  Member "learning from between class examples for deep sound recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and enhancing mixed sample data augmentation")
  Member "deep metric learning with angular loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "representation learning of music using artist labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zero shot learning for audio based music classification and tagging")
  Member "embedding label structures for fine grained feature representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")

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.SD(Sound)