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 "enhanced image coverage using evolutionary keypoint selection"):
  Abstract: ABSTRACTCoverage of image features plays an important role in many vision algorithms such as homography estimation since their distribution affects the accuracy of the estimated homography. This paper presents an evolutionary algorithm, namely genetic algorithm, in order to select the optimal set of features yielding maximum coverage of the image. The coverage metric employed in the study is a robust method based on spatial statistics. A chromosome structure was designed to indicate whether the image features will be employed in the coverage computation or not. Genetic operators such as recombination or mutations were employed to search for different sets of features. The paper shows evaluation results with statistical tests on two datasets. Results indicate that the approach can find the set of features that generate higher coverage values and this finding was also confirmed by an accuracy test on the computed homography for the original set of features and the newly selected set. Results also demonstrat...

Includes members:
  Member "is hamming distance only way for matching binary image feature descriptors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bit planes dense subpixel alignment of binary descriptors")
  Member "rapid online analysis of local feature detectors and their complementarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic selection of the optimal local feature detector")
  Member "faster and better a machine learning approach to corner detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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)