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 "enhancing music features by knowledge transfer from user item log data"):
  Abstract: In this paper, we propose a novel method that exploits music listening log data for general-purpose music feature extraction. Despite the wealth of information available in the log data of user-item interactions, it has been mostly used for collaborative filtering to find similar items or users and was not fully investigated for content-based music applications. We resolve this problem by extending intra-domain knowledge distillation to cross-domain: i.e., by transferring knowledge obtained from the user-item domain to the music content domain. The proposed system first trains the model that estimates log information from the audio contents; then it uses the model to improve other task-specific models. The experiments on various music classification and regression tasks show that the proposed method successfully improves the performances of the task-specific models.

Includes members:
  Member "deep content user embedding model for music recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving collaborative metric learning with efficient negative sampling")
  Member "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "toward interpretable music tagging with self attention")
  Member "fma a dataset for music analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for audio signal processing")
  Member "convolutional recurrent neural networks for music classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "music2dance dancenet for music driven dance generation")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)