human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "physiological and affective computing through thermal imaging a survey"):
  Abstract: Thermal imaging-based physiological and affective computing is an emerging research area enabling technologies to monitor our bodily functions and understand psychological and affective needs in a contactless manner. However, up to recently, research has been mainly carried out in very controlled lab settings. As small size and even low-cost versions of thermal video cameras have started to appear on the market, mobile thermal imaging is opening its door to ubiquitous and real-world applications. Here we review the literature on the use of thermal imaging to track changes in physiological cues relevant to affective computing and the technological requirements set so far. In doing so, we aim to establish computational and methodological pipelines from thermal images of the human skin to affective states and outline the research opportunities and challenges to be tackled to make ubiquitous real-life thermal imaging-based affect monitoring a possibility.

Includes members:
  Member "nose heat exploring stress induced nasal thermal variability through mobile thermal imaging":
    also connected through other hyperedges:
      (none)
  Member "deep thermal imaging proximate material type recognition in the wild through deep learning of spatial surface temperature patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detection and identification of objects and humans in thermal images")
  Member "thermsense smartphone based breathing sensing platform using noncontact low cost thermal camera":
    also connected through other hyperedges:
      (none)
  Member "deepbreath deep learning of breathing patterns for automatic stress recognition using low cost thermal imaging in unconstrained settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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")
  Member "robust tracking of respiratory rate in high dynamic range scenes using mobile thermal imaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nose heat exploring stress induced nasal thermal variability through mobile thermal imaging")

Question: Please tell me which class the source paper 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)