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: "visual to sound generating natural sound for videos in the wild"
  Abstract: As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlated during natural events, these two modalities combine to jointly affect human perception. In this paper, we pose the task of generating sound given visual input. Such capabilities could help enable applications in virtual reality (generating sound for virtual scenes automatically) or provide additional accessibility to images or videos for people with visual impairments. As a first step in this direction, we apply learning-based methods to generate raw waveform samples given input video frames. We evaluate our models on a dataset of videos containing a variety of sounds (such as ambient sounds and sounds from people/animals). Our experiments show that the generated sounds are fairly realistic and have good temporal synchronization with the visual inputs.

Connected through hyperedge HE-1 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "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"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-2 (induced by source paper "listen to look action recognition by previewing audio"):
  with members:
    - "what makes training multi modal networks hard"
    - "learning to localize sound sources in visual scenes analysis and applications"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "distinit learning video representations without a single labeled video"
    - "co separating sounds of visual objects"
    - "videobert a joint model for video and language representation learning"
    - "temporal cycle consistency learning"

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 "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning to have an ear for face super resolution"):
  with members:
    - "seeing what a gan cannot generate"
    - "what makes training multi modal networks hard"
    - "toward real world single image super resolution a new benchmark and a new model"
    - "two stream action recognition oriented video super resolution"
    - "image super resolution by neural texture transfer"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "zoom to learn learn to zoom"

Connected through hyperedge HE-7 (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"
    - "learning to localize sound source in visual scenes"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "dancing to music"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "self supervised audio spatialization with correspondence classifier"
    - "video to video synthesis"
    - "learning to localize sound source in visual scenes"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "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"
    - "jointly discovering visual objects and spoken words from raw sensory input"

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)