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 audio visual generation"
  Abstract: Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio/visual, from the other modality, i.e., visual/audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space.

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

Connected through hyperedge HE-2 (induced by source paper "tailorgan making user defined fashion designs"):
  with members:
    - "lip movements generation at a glance"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "sequential attention gan for interactive image editing via dialogue"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-3 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-4 (induced by source paper "vision infused deep audio inpainting"):
  with members:
    - "compound domain adaptation in an open world"
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"

Connected through hyperedge HE-5 (induced by source paper "earthquakegen earthquake simulation using generative adversarial networks"):
  with members:
    - "deepdetect a cascaded region based densely connected network for seismic event detection"
    - "gansynth adversarial neural audio synthesis"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-6 (induced by source paper "leveraging pretrained image classifiers for language based segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to exploit the prior network knowledge for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "rtseg real time semantic segmentation comparative study"
    - "taskonomy disentangling task transfer learning"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "object level representation learning for few shot image classification"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"

Connected through hyperedge HE-7 (induced by source paper "cascade attention guided residue learning gan for cross modal translation"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "mirrorgan learning text to image generation by redescription"
    - "self supervised audio visual co segmentation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "towards audio to scene image synthesis using generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "co separating sounds of visual objects"):
  with members:
    - "the sound of motions"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual event localization in unconstrained videos"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"
    - "2 5d visual sound"

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.CV(Computer Vision and Pattern Recognition)