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: "the conversation deep audio visual speech enhancement"
  Abstract: Our goal is to isolate individual speakers from multi-talker simultaneous speech in videos. Existing works in this area have focussed on trying to separate utterances from known speakers in controlled environments. In this paper, we propose a deep audio-visual speech enhancement network that is able to separate a speaker's voice given lip regions in the corresponding video, by predicting both the magnitude and the phase of the target signal. The method is applicable to speakers unheard and unseen during training, and for unconstrained environments. We demonstrate strong quantitative and qualitative results, isolating extremely challenging real-world examples.

Connected through hyperedge HE-1 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders"
    - "a deep generative model of speech complex spectrograms"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "end to end audiovisual speech recognition"
    - "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"

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 "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "mfas multimodal fusion architecture search"
    - "centralnet a multilayer approach for multimodal fusion"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "view adaptive neural networks for high performance skeleton based human action recognition"

Connected through hyperedge HE-4 (induced by source paper "learning to localize sound sources in visual scenes analysis and applications"):
  with members:
    - "audio visual model distillation using acoustic images"
    - "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"
    - "the sound of pixels"

Connected through hyperedge HE-5 (induced by source paper "controllable attention for structured layered video decomposition"):
  with members:
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "learning to localize sound source in visual scenes"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "a short note about kinetics 600"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-6 (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"
    - "self supervised audio visual co segmentation"
    - "audio visual embodied navigation"
    - "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-7 (induced by source paper "recursive visual sound separation using minus plus net"):
  with members:
    - "feature intertwiner for object detection"
    - "learning to localize sound source in visual scenes"
    - "a neural compositional paradigm for image captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "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"
    - "neural network encapsulation"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-8 (induced by source paper "co separating sounds of visual objects"):
  with members:
    - "the sound of motions"
    - "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"
    - "the sound of pixels"

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)