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 "noise influence on the fuzzy linguistic partitioning of iris code space"):
  Abstract: This paper analyses the set of iris codes stored or used in an iris recognition system as an f-granular space. The f-granulation is given by identifying in the iris code space the extensions of the fuzzy concepts wolves, goats, lambs and sheep (previously introduced by Doddington as 'animals' of the biometric menagerie) - which together form a partitioning of the iris code space. The main question here is how objective (stable / stationary) this partitioning is when the iris segments are subject to noisy acquisition. In order to prove that the f-granulation of iris code space with respect to the fuzzy concepts that define the biometric menagerie is unstable in noisy conditions (is sensitive to noise), three types of noise (localvar, motion blur, salt and pepper) have been alternatively added to the iris segments extracted from University of Bath Iris Image Database. The results of 180 exhaustive (all-to-all) iris recognition tests are presented and commented here.

Includes members:
  Member "combined haar hilbert and log gabor based iris encoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "examples of artificial perceptions in optical character recognition and iris recognition")
  Member "8 valent fuzzy logic for iris recognition and biometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "examples of artificial perceptions in optical character recognition and iris recognition")
  Member "iris codes classification using discriminant and witness directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relevant features for gender classification in nir periocular images")
  Member "comparing haar hilbert and log gabor based iris encoders on bath iris image database":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combined haar hilbert and log gabor based iris encoders")
  Member "exploring new directions in iris recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combined haar hilbert and log gabor based iris encoders")

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)