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: "an on line variational bayesian model for multi person tracking from cluttered scenes"
  Abstract: We proposed a variational Bayesian model for time varying number of person tracking.Our tracker exploit cluttered visual observations obtained from person detectors.People entering and leaving scene are handled with birth and visibility processes.Competitive results are achieved over on classical tracking benchmarks datasets. Display Omitted Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions. We propose a variational Bayesian framework for tracking an unknown and varying number of persons. Our model results in a variational expectation-maximization (VEM) algorithm with closed-form expressions both for the posterior distributions of the latent variables and for the estimation of the model parameters. The proposed model exploits observations from multiple detectors, and it is therefore multimodal by nature. Finally, we propose to embed both object-birth and object-visibility processes in an effort to robustly handle temporal appearances and disappearances. Evaluated on classical multiple person tracking datasets, our method shows competitive results with respect to state-of-the-art multiple-object tracking algorithms, such as the probability hypothesis density (PHD) filter, among others.

Connected through hyperedge HE-1 (induced by source paper "how to train your deep multi object tracker"):
  with members:
    - "tracking without bells and whistles"
    - "mots multi object tracking and segmentation"
    - "online multi object tracking with dual matching attention networks"
    - "combined image and world space tracking in traffic scenes"
    - "distractor aware siamese networks for visual object tracking"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-2 (induced by source paper "extended gaze following detecting objects in videos beyond the camera field of view"):
  with members:
    - "neural network based reinforcement learning for audio visual gaze control in human robot interaction"
    - "deep visual attention prediction"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "tracking gaze and visual focus of attention of people involved in social interaction"
    - "eye tracking for everyone"
    - "u net convolutional networks for biomedical image segmentation"
    - "appearance based gaze estimation in the wild"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "online localization and tracking of multiple moving speakers in reverberant environments"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "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"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "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"
    - "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"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "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 multiple audio sources with the von mises distribution and variational em"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "variational bayesian inference for audio visual tracking of multiple speakers"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"

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)