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: "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"
  Abstract: We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalised self-report as ground truth, the CNN reaches 84.59% accuracy in discriminating between two levels of stress and 56.52% in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Finally, the dataset of labelled thermal images will be open to the community.

Connected through hyperedge HE-1 (induced by source paper "abnormal respiratory patterns classifier may contribute to large scale screening of people infected with covid 19 in an accurate and unobtrusive manner"):
  with members:
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (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"
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"

Connected through hyperedge HE-3 (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"
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"

Connected through hyperedge HE-4 (induced by source paper "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"):
  with members:
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"
    - "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-5 (induced by source paper "thermsense smartphone based breathing sensing platform using noncontact low cost thermal camera"):
  with members:
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"
    - "realpen providing realism in handwriting tasks on touch surfaces using auditory tactile feedback"

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.HC(Human-Computer Interaction)