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: "tracking gaze and visual focus of attention of people involved in social interaction"
  Abstract: The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model (also referred switching Kalman filter). The proposed formulation leads to a tractable learning method and to an efficient online inference procedure that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets, Vernissage and LAEO, that contain typical multi-party human-robot and human-human interactions.

Connected through hyperedge HE-1 (induced by source paper "detecting attended visual targets in video"):
  with members:
    - "laeo net revisiting people looking at each other in videos"
    - "extended gaze following detecting objects in videos beyond the camera field of view"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "ssd single shot multibox detector"
    - "deep residual learning for image recognition"

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"
    - "eye tracking for everyone"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "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 "laeo net revisiting people looking at each other in videos"):
  with members:
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "learnable pins cross modal embeddings for person identity"
    - "fine grained head pose estimation without keypoints"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "ssd single shot multibox detector"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "anticipating visual representations from unlabeled video"

Connected through hyperedge HE-4 (induced by source paper "improving temporal interpolation of head and body pose using gaussian process regression in a matrix completion setting"):
  with members:
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "using inertial sensors for position and orientation estimation"
    - "convolutional pose machines"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deeppose human pose estimation via deep neural networks"
    - "the power of convex relaxation near optimal matrix completion"

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)