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: "event retrieval using motion barcodes"
  Abstract: We introduce a simple and effective method for retrieval of videos showing a specific event, even when the videos of that event were captured from significantly different viewpoints. Appearance-based methods fail in such cases, as appearances change with large changes of viewpoints. #R##N#Our method is based on a pixel-based feature, "motion barcode", which records the existence/non-existence of motion as a function of time. While appearance, motion magnitude, and motion direction can vary greatly between disparate viewpoints, the existence of motion is viewpoint invariant. Based on the motion barcode, a similarity measure is developed for videos of the same event taken from very different viewpoints. This measure is robust to occlusions common under different viewpoints, and can be computed efficiently. #R##N#Event retrieval is demonstrated using challenging videos from stationary and hand held cameras.

Connected through hyperedge HE-1 (induced by source paper "an epipolar line from a single pixel"):
  with members:
    - "epipolar geometry based on line similarity"
    - "fundamental matrices from moving objects using line motion barcodes"
    - "camera calibration from dynamic silhouettes using motion barcodes"

Connected through hyperedge HE-2 (induced by source paper "camera calibration by global constraints on the motion of silhouettes"):
  with members:
    - "fundamental matrices from moving objects using line motion barcodes"
    - "camera calibration from dynamic silhouettes using motion barcodes"

Connected through hyperedge HE-3 (induced by source paper "fundamental matrices from moving objects using line motion barcodes"):
  with members:
    - "camera calibration from dynamic silhouettes using motion barcodes"
    - "epipolar geometry from temporal signatures and dynamic silhouettes"

Connected through hyperedge HE-4 (induced by source paper "epipolar geometry based on line similarity"):
  with members:
    - "camera calibration from dynamic silhouettes using motion barcodes"

Connected through hyperedge HE-5 (induced by source paper "two points fundamental matrix"):
  with members:
    - "camera calibration from dynamic silhouettes 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)