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: "combined haar hilbert and log gabor based iris encoders"
  Abstract: This chapter shows that combining Haar-Hilbert and Log-Gabor improves iris recognition performance leading to a less ambiguous biometric decision landscape in which the overlap between the experimental intra- and interclass score distributions diminishes or even vanishes. Haar-Hilbert, Log-Gabor and combined Haar-Hilbert and Log-Gabor encoders are tested here both for single and dual iris approach. The experimental results confirm that the best performance is obtained for the dual iris approach when the iris code is generated using the combined Haar-Hilbert and Log-Gabor encoder, and when the matching score fuses the information from both Haar-Hilbert and Log-Gabor channels of the combined encoder.

Connected through hyperedge HE-1 (induced by source paper "examples of artificial perceptions in optical character recognition and iris recognition"):
  with members:
    - "noise influence on the fuzzy linguistic partitioning of iris code space"
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "iris codes classification using discriminant and witness directions"
    - "from cognitive binary logic to cognitive intelligent agents"
    - "handwritten farsi character recognition using artificial neural network"

Connected through hyperedge HE-2 (induced by source paper "the biometric menagerie a fuzzy and inconsistent concept"):
  with members:
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "iris codes classification using discriminant and witness directions"
    - "comparing haar hilbert and log gabor based iris encoders on bath iris image database"
    - "exploring new directions in iris recognition"

Connected through hyperedge HE-3 (induced by source paper "noise influence on the fuzzy linguistic partitioning of iris code space"):
  with members:
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "iris codes classification using discriminant and witness directions"
    - "comparing haar hilbert and log gabor based iris encoders on bath iris image database"
    - "exploring new directions in iris recognition"

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)