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: "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"
  Abstract: The ability to monitor the respiratory rate, one of the vital signs, is extremely important for the medical treatment, healthcare and fitness sectors. In many situations, mobile methods, which allow users to undertake everyday activities, are required. However, current monitoring systems can be obtrusive, requiring users to wear respiration belts or nasal probes. Alternatively, contactless digital image sensor based remote-photoplethysmography (PPG) can be used. However, remote PPG requires an ambient source of light, and does not work properly in dark places or under varying lighting conditions. Recent advances in thermographic systems have shrunk their size, weight and cost, to the point where it is possible to create smart-phone based respiration rate monitoring devices that are not affected by lighting conditions. However, mobile thermal imaging is challenged in scenes with high thermal dynamic ranges (e.g. due to the different environmental temperature distributions indoors and outdoors). This challenge is further amplified by general problems such as motion artifacts and low spatial resolution, leading to unreliable breathing signals. In this paper, we propose a novel and robust approach for respiration tracking which compensates for the negative effects of variations in the ambient temperature and motion artifacts and can accurately extract breathing rates in highly dynamic thermal scenes. The approach is based on tracking the nostril of the user and using local temperature variations to infer inhalation and exhalation cycles. It has three main contributions. The first is a novel Optimal Quantization technique which adaptively constructs a color mapping of absolute temperature to improve segmentation, classification and tracking. The second is the Thermal Gradient Flow method that computes thermal gradient magnitude maps to enhance the accuracy of the nostril region tracking. Finally, we introduce the Thermal Voxel method to increase the reliability of the captured respiration signals compared to the traditional averaging method. We demonstrate the extreme robustness of our system to track the nostril-region and measure the respiratory rate by evaluating it during controlled respiration exercises in high thermal dynamic scenes (e.g. strong correlation (r = 0.9987) with the ground truth from the respiration-belt sensor). We also demonstrate how our algorithm outperformed standard algorithms in settings with different amounts of environmental thermal changes and human motion. We open the tracked ROI sequences of the datasets collected for these studies (i.e. under both controlled and unconstrained real-world settings) to the community to foster work in this area.

Connected through hyperedge HE-1 (induced by source paper "physiological and affective computing through thermal imaging a survey"):
  with members:
    - "nose heat exploring stress induced nasal thermal variability through mobile thermal imaging"
    - "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"
    - "thermsense smartphone based breathing sensing platform using noncontact low cost thermal camera"
    - "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"

Connected through hyperedge HE-2 (induced by source paper "nose heat exploring stress induced nasal thermal variability through mobile thermal imaging"):
  with members:
    - "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"
    - "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"

Connected through hyperedge HE-3 (induced by source paper "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"):
  with members:
    - "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"
    - "realpen providing realism in handwriting tasks on touch surfaces using auditory tactile feedback"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "spatial transformer networks"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-4 (induced by source paper "thermsense smartphone based breathing sensing platform using noncontact low cost thermal camera"):
  with members:
    - "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"
    - "realpen providing realism in handwriting tasks on touch surfaces using auditory tactile feedback"

Connected through hyperedge HE-5 (induced by source paper "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"):
  with members:
    - "going deeper with convolutions"
    - "network in network"

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)