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: "improved repeatability measures for evaluating performance of feature detectors"
  Abstract: The most frequently employed measure for performance characterisation of local feature detectors is repeatability, but it has been observed that this does not necessarily mirror actual performance. Presented are improved repeatability formulations which correlate much better with the true performance of feature detectors. Comparative results for several state-of-the-art feature detectors are presented using these measures; it is found that Hessian-based detectors are generally superior at identifying features when images are subject to various geometric and photometric transformations.

Connected through hyperedge HE-1 (induced by source paper "automatic selection of the optimal local feature detector"):
  with members:
    - "assessing the performance bounds of local feature detectors taking inspiration from electronics design practices"
    - "performance characterization of image feature detectors in relation to the scene content utilizing a large image database"
    - "rapid online analysis of local feature detectors and their complementarity"

Connected through hyperedge HE-2 (induced by source paper "a generic framework for assessing the performance bounds of image feature detectors"):
  with members:
    - "rapid online analysis of local feature detectors and their complementarity"

Connected through hyperedge HE-3 (induced by source paper "assessing the performance bounds of local feature detectors taking inspiration from electronics design practices"):
  with members:
    - "rapid online analysis of local feature detectors and their complementarity"

Connected through hyperedge HE-4 (induced by source paper "performance characterization of image feature detectors in relation to the scene content utilizing a large image database"):
  with members:
    - "rapid online analysis of local feature detectors and their complementarity"

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)