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: "audio visual speaker diarization based on spatiotemporal bayesian fusion"
  Abstract: Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem. The latter is solved within a novel audio-visual fusion method on the following grounds: binaural spectral features are first extracted from a microphone pair, then a supervised audio-visual alignment technique maps these features onto an image, and finally a semi-supervised clustering method assigns binaural spectral features to visible persons. The main advantage of this method over previous work is that it processes in a principled way speech signals uttered simultaneously by multiple persons. The diarization itself is cast into a latent-variable temporal graphical model that infers speaker identities and speech turns, based on the output of an audio-visual association process, executed at each time slice, and on the dynamics of the diarization variable itself. The proposed formulation yields an efficient exact inference procedure. A novel dataset, that contains audio-visual training data as well as a number of scenarios involving several participants engaged in formal and informal dialogue, is introduced. The proposed method is thoroughly tested and benchmarked with respect to several state-of-the art diarization algorithms.

Connected through hyperedge HE-1 (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 scene analysis with self supervised multisensory features"
    - "automated curriculum learning for neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "visual speech enhancement"

Connected through hyperedge HE-2 (induced by source paper "neural network based reinforcement learning for audio visual gaze control in human robot interaction"):
  with members:
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "show attend and interact perceivable human robot social interaction through neural attention q network"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "online localization and tracking of multiple moving speakers in reverberant environments"):
  with members:
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-4 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "tracking multiple audio sources with the von mises distribution and variational em"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "mot16 a benchmark for multi object tracking"
    - "person re identification in the wild"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-5 (induced by source paper "a cascaded multiple speaker localization and tracking system"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "estimation of the direct path relative transfer function for supervised sound source localization"
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"

Connected through hyperedge HE-6 (induced by source paper "tracking gaze and visual focus of attention of people involved in social interaction"):
  with members:
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "eye tracking for everyone"

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)