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: "ava activespeaker an audio visual dataset for active speaker detection"
  Abstract: Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual dataset for this task has constrained algorithm evaluations with respect to data diversity, environments, and accuracy. This has made comparisons and improvements difficult. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) that will be released publicly to facilitate algorithm development and enable comparisons. The dataset contains temporally labeled face tracks in video, where each face instance is labeled as speaking or not, and whether the speech is audible. This dataset contains about 3.65 million human labeled frames or about 38.5 hours of face tracks, and the corresponding audio. We also present a new audio-visual approach for active speaker detection, and analyze its performance, demonstrating both its strength and the contributions of the dataset.

Connected through hyperedge HE-1 (induced by source paper "bio inspired modality fusion for active speaker detection"):
  with members:
    - "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"
    - "cross modal supervision for learning active speaker detection in video"
    - "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 "crossmodal learning for audio visual speech event localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ava speech a densely labeled dataset of speech activity in movies"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "objects that sound"
    - "look listen and learn"

Connected through hyperedge HE-3 (induced by source paper "naver at activitynet challenge 2019 task b active speaker detection ava"):
  with members:
    - "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"
    - "cross modal supervision for learning active speaker detection in video"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"

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)