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: "rapid online analysis of local feature detectors and their complementarity"
  Abstract: A vision system that can assess its own performance and take appropriate actions online to maximize its effectiveness would be a step towards achieving the long-cherished goal of imitating humans. This paper proposes a method for performing an online performance analysis of local feature detectors, the primary stage of many practical vision systems. It advocates the spatial distribution of local image features as a good performance indicator and presents a metric that can be calculated rapidly, concurs with human visual assessments and is complementary to existing offline measures such as repeatability. The metric is shown to provide a measure of complementarity for combinations of detectors, correctly reflecting the underlying principles of individual detectors. Qualitative results on well-established datasets for several state-of-the-art detectors are presented based on the proposed measure. Using a hypothesis testing approach and a newly-acquired, larger image database, statistically-significant performance differences are identified. Different detector pairs and triplets are examined quantitatively and the results provide a useful guideline for combining detectors in applications that require a reasonable spatial distribution of image features. A principled framework for combining feature detectors in these applications is also presented. Timing results reveal the potential of the metric for online applications.

Connected through hyperedge HE-1 (induced by source paper "enhanced image coverage using evolutionary keypoint selection"):
  with members:
    - "is hamming distance only way for matching binary image feature descriptors"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (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"
    - "improved repeatability measures for evaluating performance of feature detectors"
    - "performance characterization of image feature detectors in relation to the scene content utilizing a large image database"

Connected through hyperedge HE-3 (induced by source paper "a generic framework for assessing the performance bounds of image feature detectors"):
  with members:
    - "improved repeatability measures for evaluating performance of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "assessing the performance bounds of local feature detectors taking inspiration from electronics design practices"):
  with members:
    - "improved repeatability measures for evaluating performance of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "performance characterization of image feature detectors in relation to the scene content utilizing a large image database"):
  with members:
    - "improved repeatability measures for evaluating performance of feature detectors"

Connected through hyperedge HE-6 (induced by source paper "comparing feature detectors a bias in the repeatability criteria and how to correct it"):
  with members:
    - "representation learning a review and new perspectives"
    - "faster and better a machine learning approach to corner detection"

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)