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: "on learning associations of faces and voices"
  Abstract: In this paper, we study the associations between human faces and voices. Audiovisual integration, specifically the integration of facial and vocal information is a well-researched area in neuroscience. It is shown that the overlapping information between the two modalities plays a significant role in perceptual tasks such as speaker identification. Through an online study on a new dataset we created, we confirm previous findings that people can associate unseen faces with corresponding voices and vice versa with greater than chance accuracy. We computationally model the overlapping information between faces and voices and show that the learned cross-modal representation contains enough information to identify matching faces and voices with performance similar to that of humans. Our representation exhibits correlations to certain demographic attributes and features obtained from either visual or aural modality alone. We release our dataset of audiovisual recordings and demographic annotations of people reading out short text used in our studies.

Connected through hyperedge HE-1 (induced by source paper "learning to localize sound sources in visual scenes analysis and applications"):
  with members:
    - "audio visual model distillation using acoustic images"
    - "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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "speech2face learning the face behind a voice"):
  with members:
    - "diversity in faces"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "speech driven expressive talking lips with conditional sequential generative adversarial networks"
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "emotion recognition in speech using cross modal transfer in the wild"

Connected through hyperedge HE-3 (induced by source paper "deep latent space learning for cross modal mapping of audio and visual signals"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "audio visual event localization in unconstrained videos"
    - "learnable pins cross modal embeddings for person identity"
    - "git loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-4 (induced by source paper "reconstructing faces from voices"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "learnable pins cross modal embeddings for person identity"
    - "towards open set identity preserving face synthesis"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-5 (induced by source paper "voice face cross modal matching and retrieval a benchmark"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "disjoint mapping network for cross modal matching of voices and faces"
    - "arcface additive angular margin loss for deep face recognition"
    - "learnable pins cross modal embeddings for person identity"
    - "voxceleb2 deep speaker recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep speaker an end to end neural speaker embedding system"

Connected through hyperedge HE-6 (induced by source paper "emotion recognition in speech using cross modal transfer in the wild"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "deep learning is robust to massive label noise"
    - "self supervised learning of a facial attribute embedding from video"
    - "learnable pins cross modal embeddings for person identity"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "exploring the limits of weakly supervised pretraining"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "look listen and learn"

Connected through hyperedge HE-7 (induced by source paper "noise tolerant audio visual online person verification using an attention based neural network fusion"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "end to end audiovisual speech recognition"
    - "voxceleb2 deep speaker recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "voxceleb a large scale speaker identification dataset"
    - "robust end to end deep audiovisual speech recognition"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Connected through hyperedge HE-8 (induced by source paper "perfect match improved cross modal embeddings for audio visual synchronisation"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "dynamic temporal alignment of speech to lips"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "objects that sound"
    - "lip reading sentences in the wild"
    - "context encoders feature learning by inpainting"

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)