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 separate object sounds by watching unlabeled video"
  Abstract: Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources together. We propose to learn audio-visual object models from unlabeled video, then exploit the visual context to perform audio source separation in novel videos. Our approach relies on a deep multi-instance multi-label learning framework to disentangle the audio frequency bases that map to individual visual objects, even without observing/hearing those objects in isolation. We show how the recovered disentangled bases can be used to guide audio source separation to obtain better-separated, object-level sounds. Our work is the first to study audio source separation in large-scale general "in the wild" videos. We obtain state-of-the-art results on visually-aided audio source separation and audio denoising.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"

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"
    - "learning to localize sound source in visual scenes"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "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"
    - "learning to localize sound source in visual scenes"
    - "deep multimodal clustering for unsupervised audiovisual learning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-4 (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"
    - "learning to localize sound source in visual scenes"

Connected through hyperedge HE-5 (induced by source paper "learning multi instrument classification with partial labels"):
  with members:
    - "an attention mechanism for musical instrument recognition"
    - "frame level instrument recognition by timbre and pitch"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "focal loss for dense object detection"
    - "audio set classification with attention model a probabilistic perspective"
    - "mixup beyond empirical risk minimization"
    - "cnn architectures for large scale audio classification"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-6 (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-7 (induced by source paper "self supervised learning of pretext invariant representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "s4l self supervised semi supervised learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-8 (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"

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)