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: "self supervised audio visual co segmentation"
  Abstract: Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data. In this paper we develop a neural network model for visual object segmentation and sound source separation that learns from natural videos through self-supervision. The model is an extension of recently proposed work that maps image pixels to sounds. Here, we introduce a learning approach to disentangle concepts in the neural networks, and assign semantic categories to network feature channels to enable independent image segmentation and sound source separation after audio-visual training on videos. Our evaluations show that the disentangled model outperforms several baselines in semantic segmentation and sound source separation.

Connected through hyperedge HE-1 (induced by source paper "self supervised learning by cross modal audio video clustering"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "self supervised learning for video correspondence flow"
    - "learning correspondence from the cycle consistency of time"
    - "video representation learning by dense predictive coding"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "randomly weighted cnns for music audio classification"
    - "scaling egocentric vision the epic kitchens dataset"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-3 (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"
    - "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"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-4 (induced by source paper "look listen and act towards audio visual embodied navigation"):
  with members:
    - "self supervised moving vehicle tracking with stereo sound"
    - "the sound of motions"
    - "audio visual embodied navigation"
    - "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"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"

Connected through hyperedge HE-5 (induced by source paper "self supervised moving vehicle tracking with stereo sound"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "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"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-6 (induced by source paper "deep bayesian unsupervised source separation based on a complex gaussian mixture model"):
  with members:
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "unsupervised training of neural mask based beamforming"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "bootstrapping single channel source separation via unsupervised spatial clustering on stereo mixtures"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "adam a method for stochastic optimization"

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)