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 "sub pixel matching method for low resolution thermal stereo images"):
  Abstract: In the context of a localization and tracking application, we developed a stereo vision system based on cheap low-resolution 80x60 pixels thermal cameras. We proposed a threefold sub-pixel stereo matching framework (called ST for Subpixel Thermal): 1) robust features extraction method based on phase congruency, 2) rough matching of these features in pixel precision, and 3) refined matching in sub-pixel accuracy based on local phase coherence. We performed experiments on our very low-resolution thermal images (acquired using a stereo system we manufactured) as for high-resolution images from a benchmark dataset. Even if phase congruency computation time is high, it was able to extract two times more features than state-of-the-art methods such as ORB or SURF. We proposed a modified version of the phase correlation applied in the phase congruency feature space for sub-pixel matching. Using simulated stereo, we investigated how the phase congruency threshold and the sub-image size of sub-pixel matching can influence the accuracy. We then proved that given our stereo setup and the resolution of our images, being wrong of 1 pixel leads to a 500 mm error in the Z position of the point. Finally, we showed that our method could extract four times more matches than a baseline method ORB + OpenCV KNN matching on low-resolution images. Moreover, our matches were more robust. More precisely, when projecting points of a standing person, ST got a standard deviation of 300 mm when ORB + OpenCV KNN gave more than 1000 mm.

Includes members:
  Member "image matching using sift surf brief and orb performance comparison for distorted images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating a null model of scientific image reuse to support research integrity investigations")
  Member "faster and better a machine learning approach to corner detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comparative study for non rigid image registration and rigid image registration")

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.CV(Computer Vision and Pattern Recognition)