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: "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns"
  Abstract: We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognize 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.

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"
    - "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"
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"

Connected through hyperedge HE-2 (induced by source paper "detection and identification of objects and humans in thermal images"):
  with members:
    - "unsupervised segmentation of fire and smoke from infra red videos"
    - "deep neural networks for pattern recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "nose heat exploring stress induced nasal thermal variability through mobile thermal imaging"):
  with members:
    - "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings"
    - "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging"

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)