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: "learning to localize sound source in visual scenes"
  Abstract: Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene pairs like human? In this paper, we propose a novel unsupervised algorithm to address the problem of localizing the sound source in visual scenes. A two-stream network structure which handles each modality, with attention mechanism is developed for sound source localization. Moreover, although our network is formulated within the unsupervised learning framework, it can be extended to a unified architecture with a simple modification for the supervised and semi-supervised learning settings as well. Meanwhile, a new sound source dataset is developed for performance evaluation. Our empirical evaluation shows that the unsupervised method eventually go through false conclusion in some cases. We show that even with a few supervision, false conclusion is able to be corrected and the source of sound in a visual scene can be localized effectively.

Connected through hyperedge HE-1 (induced by source paper "stavis spatio temporal audiovisual saliency network"):
  with members:
    - "dave a deep audio visual embedding for dynamic saliency prediction"
    - "the sound of motions"
    - "deep visual attention prediction"
    - "deepvs a deep learning based video saliency prediction approach"
    - "audio visual event localization in unconstrained videos"
    - "end to end saliency mapping via probability distribution prediction"
    - "susinet see understand and summarize it"
    - "the sound of pixels"

Connected through hyperedge HE-2 (induced by source paper "semantic object prediction and spatial sound super resolution with binaural sounds"):
  with members:
    - "self supervised moving vehicle tracking with stereo sound"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "scene aware audio for 360 videos"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "learning to separate object sounds by watching unlabeled video"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the sound of pixels"

Connected through hyperedge HE-3 (induced by source paper "curriculum audiovisual learning"):
  with members:
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "deep multimodal clustering for unsupervised audiovisual learning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-4 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning"):
  with members:
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "multimodal machine learning a survey and taxonomy"
    - "fast slow recurrent neural networks"
    - "look listen and learn"
    - "aenet learning deep audio features for video analysis"
    - "video frame interpolation via adaptive separable convolution"
    - "visual to sound generating natural sound for videos in the wild"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-5 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders"):
  with members:
    - "stargan vc2 rethinking conditional methods for stargan based voice conversion"
    - "cyclegan vc2 improved cyclegan based non parallel voice conversion"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "text based editing of talking head video"
    - "learning individual styles of conversational gesture"
    - "speech2face learning the face behind a voice"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "seeing voices and hearing faces cross modal biometric matching"

Connected through hyperedge HE-6 (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-7 (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"
    - "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"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "the sound of motions"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "self supervised audio visual co segmentation"
    - "end to end learning of motion representation for video understanding"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "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"
    - "learning to separate object sounds by watching unlabeled video"

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)