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 "deep cross modal correlation learning for audio and lyrics in music retrieval"):
  Abstract: Little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics are taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Different modality data are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study on understanding the correlation between language and music audio through deep architectures for learning the paired temporal correlation of audio and lyrics. Pre-trained Doc2vec model followed by fully-connected layers (fully-connected deep neural network) is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) pre-trained CNN followed by fully-connected layers is investigated for representing music audio. ii) We further suggest an end-to-end architecture that simultaneously trains convolutional layers and fully-connected layers to better learn temporal structures of music audio. Particularly, our end-to-end deep architecture contains two properties: simultaneously implementing feature learning and cross-modal correlation learning, and learning joint representation by considering temporal structures. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval.

Includes members:
  Member "category based deep cca for fine grained venue discovery from multimodal data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video face super resolution with motion adaptive feedback cell")
  Member "lyrics based music genre classification using a hierarchical attention network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "brazilian lyrics based music genre classification using a blstm network")
  Member "sample level deep convolutional neural networks for music auto tagging using raw waveforms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for audio signal processing")
  Member "instance aware image and sentence matching with selective multimodal lstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive offline quintuplet loss for image text matching")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal dense video captioning")
  Member "an empirical evaluation of doc2vec with practical insights into document embedding generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "can x2vec save lives integrating graph and language embeddings for automatic mental health classification")
  Member "multi view recurrent neural acoustic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effectiveness of self supervised pre training for speech recognition")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for audio signal processing")

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)