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: "epipolar geometry from temporal signatures and dynamic silhouettes"
  Abstract: Epipolar geometry between two images is normally computed by matching feature points between the images. When not enough feature points can be found, e.g. when the camera viewpoints vary greatly, epipolar geometry can be computed from video sequences showing moving objects visible in the two videos. We present a temporal signature for lines: a binary temporal sequence, indicating for each frame the existence of at least one foreground pixel on that line. We show that the temporal signatures of corresponding epipolar lines are very similar. Epipolar geometry can also be found from correspondence of epipolar lines. Correspondence of a pair of epipolar lines can be established using the similarity of their temporal signatures. This search can be limited to a small subset of the pairs of lines, since it can be shown that there exists pairs of epipolar lines each tangent to the silhouette. Compared to existing dynamic silhouette methods, using temporal signatures adds robustness, speed and accuracy. It works even when the silhouette boundaries are noisy, or the silhouette is small due to low resolution or distance of an object from the cameras

Connected through hyperedge HE-1 (induced by source paper "fundamental matrices from moving objects using line motion barcodes"):
  with members:
    - "camera calibration from dynamic silhouettes using motion barcodes"
    - "event retrieval using motion barcodes"

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)