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: "cross modal supervision for learning active speaker detection in video"
  Abstract: In this paper, we show how to use audio to supervise the learning of active speaker detection in video. Voice Activity Detection (VAD) guides the learning of the vision-based classifier in a weakly supervised manner. The classifier uses spatio-temporal features to encode upper body motion - facial expressions and gesticulations associated with speaking. We further improve a generic model for active speaker detection by learning person specific models. Finally, we demonstrate the online adaptation of generic models learnt on one dataset, to previously unseen people in a new dataset, again using audio (VAD) for weak supervision. The use of temporal continuity overcomes the lack of clean training data. We are the first to present an active speaker detection system that learns on one audio-visual dataset and automatically adapts to speakers in a new dataset. This work can be seen as an example of how the availability of multi-modal data allows us to learn a model without the need for supervision, by transferring knowledge from one modality to another.

Connected through hyperedge HE-1 (induced by source paper "bio inspired modality fusion for active speaker detection"):
  with members:
    - "ava activespeaker an audio visual dataset for active speaker detection"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "self supervised vision based detection of the active speaker as a prerequisite for socially aware language acquisition"
    - "look listen and learn a multimodal lstm for speaker identification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "naver at activitynet challenge 2019 task b active speaker detection ava"):
  with members:
    - "ava activespeaker an audio visual dataset for active speaker detection"
    - "deep audio visual speech recognition"
    - "the conversation deep audio visual speech enhancement"
    - "perfect match improved cross modal embeddings for audio visual synchronisation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "lipnet end to end sentence level lipreading"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-4 (induced by source paper "voxceleb a large scale speaker identification dataset"):
  with members:
    - "cnn architectures for large scale audio classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

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)