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: "putting a face to the voice fusing audio and visual signals across a video to determine speakers"
  Abstract: In this paper, we present a system that associates faces with voices in a video by fusing information from the audio and visual signals. The thesis underlying our work is that an extremely simple approach to generating (weak) speech clusters can be combined with visual signals to effectively associate faces and voices by aggregating statistics across a video. This approach does not need any training data specific to this task and leverages the natural coherence of information in the audio and visual streams. It is particularly applicable to tracking speakers in videos on the web where a priori information about the environment (e.g., number of speakers, spatial signals for beamforming) is not available. We performed experiments on a real-world dataset using this analysis framework to determine the speaker in a video. Given a ground truth labeling determined by human rater consensus, our approach had ~71% accuracy.

Connected through hyperedge HE-1 (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-2 (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"
    - "on learning associations of faces and voices"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "ava activespeaker an audio visual dataset for active speaker detection"):
  with members:
    - "ava speech a densely labeled dataset of speech activity in movies"
    - "the conversation deep audio visual speech enhancement"
    - "large scale visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "audio visual scene analysis with self supervised multisensory features"
    - "automated curriculum learning for neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "learning to separate object sounds by watching unlabeled video"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"

Connected through hyperedge HE-5 (induced by source paper "on learning associations of faces and voices"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "network dissection quantifying interpretability of deep visual representations"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "adversarial discriminative domain adaptation"
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"
    - "ambient sound provides supervision for visual learning"

Connected through hyperedge HE-6 (induced by source paper "ava speech a densely labeled dataset of speech activity in movies"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "deep multimodal learning for audio visual speech recognition"

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.MM(Multimedia)