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: "representation learning of music using artist labels"
  Abstract: Recently, feature representation by learning algorithms has drawn great attention. In the music domain, it is either unsupervised or supervised by semantic labels such as music genre. However, finding discriminative features in an unsupervised way is challenging, and supervised feature learning using semantic labels may involve noisy or expensive annotation. In this paper, we present a feature learning approach that utilizes artist labels attached in every single music track as an objective meta data. To this end, we train a deep convolutional neural network to classify audio tracks into a large number of artists. We regard it as a general feature extractor and apply it to artist recognition, genre classification and music auto-tagging in transfer learning settings. The results show that the proposed approach outperforms or is comparable to previous state-of-the-art methods, indicating that the proposed approach effectively captures general music audio features.

Connected through hyperedge HE-1 (induced by source paper "zero shot learning for audio based music classification and tagging"):
  with members:
    - "multi label zero shot human action recognition via joint latent embedding"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fma a dataset for music analysis"
    - "multi label zero shot learning via concept embedding"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-2 (induced by source paper "improving automatic jazz melody generation by transfer learning techniques"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "transfer learning for music classification and regression tasks"
    - "what makes imagenet good for transfer learning"
    - "song from pi a musically plausible network for pop music generation"
    - "music transcription modelling and composition using deep learning"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-3 (induced by source paper "learning a joint embedding space of monophonic and mixed music signals for singing voice"):
  with members:
    - "revisiting singing voice detection a quantitative review and the future outlook"
    - "disambiguating music artists at scale with audio metric learning"
    - "a hybrid of deep audio feature and i vector for artist recognition"
    - "learning from between class examples for deep sound recognition"
    - "deep metric learning with angular loss"
    - "embedding label structures for fine grained feature representation"

Connected through hyperedge HE-4 (induced by source paper "representation learning of music using artist album and track information"):
  with members:
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "transfer learning for music classification and regression tasks"
    - "fma a dataset for music analysis"
    - "deep learning and music adversaries"

Connected through hyperedge HE-5 (induced by source paper "zero shot learning and knowledge transfer in music classification and tagging"):
  with members:
    - "zero shot learning for audio based music classification and tagging"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "deep learning and music adversaries"

Connected through hyperedge HE-6 (induced by source paper "multi task music representation learning from multi label embeddings"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "transfer learning of artist group factors to musical genre classification"):
  with members:
    - "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fma a dataset for music analysis"
    - "theano a python framework for fast computation of mathematical expressions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "disambiguating music artists at scale with audio metric learning"):
  with members:
    - "unsupervised learning of semantic audio representations"
    - "deep metric learning with angular loss"
    - "tristounet triplet loss for speaker turn embedding"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"
    - "efficient multivariate sequence classification"
    - "large scale music annotation and retrieval learning to rank in joint semantic spaces"
    - "objective criteria for the evaluation of clustering methods"

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.SD(Sound)