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: "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.

Connected through hyperedge HE-1 (induced by source paper "video face super resolution with motion adaptive feedback cell"):
  with members:
    - "fast spatio temporal residual network for video super resolution"
    - "recurrent back projection network for video super resolution"
    - "frame recurrent video super resolution"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "cosface large margin cosine loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "residual dense network for image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-2 (induced by source paper "text2facegan face generation from fine grained textual descriptions"):
  with members:
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "face cap image captioning using facial expression analysis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "face2text collecting an annotated image description corpus for the generation of rich face descriptions"
    - "a style based generator architecture for generative adversarial networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-3 (induced by source paper "deep triplet neural networks with cluster cca for audio visual cross modal retrieval"):
  with members:
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "visual to sound generating natural sound for videos in the wild"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "fully convolutional siamese networks for object tracking"
    - "youtube 8m a large scale video classification benchmark"
    - "learning deep structure preserving image text embeddings"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "audio visual embedding for cross modal musicvideo retrieval through supervised deep cca"):
  with members:
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "youtube 8m a large scale video classification benchmark"
    - "deep cross modal hashing"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "conditional lstm gan for melody generation from lyrics"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "neural melody composition from lyrics"
    - "a hierarchical recurrent neural network for symbolic melody generation"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "algorithmic songwriting with alysia"
    - "conditional generative adversarial nets"
    - "ai methods in algorithmic composition a comprehensive survey"

Connected through hyperedge HE-6 (induced by source paper "learning embodied semantics via music and dance semiotic correlations"):
  with members:
    - "let s dance learning from online dance videos"
    - "everybody dance now"
    - "convolutional pose machines"
    - "multi view recurrent neural acoustic word embeddings"
    - "you only look once unified real time object detection"
    - "multilingual distributed representations without word alignment"

Connected through hyperedge HE-7 (induced by source paper "learning joint embedding for cross modal retrieval"):
  with members:
    - "deep triplet neural networks with cluster cca for audio visual cross modal retrieval"
    - "category based deep cca for fine grained venue discovery from multimodal data"
    - "learning from between class examples for deep sound recognition"
    - "learnable pooling with context gating for video classification"
    - "visual to sound generating natural sound for videos in the wild"

Connected through hyperedge HE-8 (induced by source paper "towards deep modeling of music semantics using eeg regularizers"):
  with members:
    - "getting closer to the essence of music the con espressione manifesto"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "representation learning of music using artist labels"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.IR(Information Retrieval)