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: "iris codes classification using discriminant and witness directions"
  Abstract: The main topic discussed in this paper is how to use intelligence for biometric decision defuzzification. A neural training model is proposed and tested here as a possible solution for dealing with natural fuzzification that appears between the intra-and inter-class distributions of scores computed during iris recognition tests. It is shown here that the use of proposed neural network support leads to an improvement in the artificial perception of the separation between the intra-and inter-class score distributions by moving them away from each other.

Connected through hyperedge HE-1 (induced by source paper "relevant features for gender classification in nir periocular images"):
  with members:
    - "predicting gender from iris texture may be harder than it seems"
    - "gender and ethnicity classification of iris images using deep class encoder"
    - "xgboost a scalable tree boosting system"
    - "pigment melanin pattern for iris recognition"

Connected through hyperedge HE-2 (induced by source paper "examples of artificial perceptions in optical character recognition and iris recognition"):
  with members:
    - "combined haar hilbert and log gabor based iris encoders"
    - "noise influence on the fuzzy linguistic partitioning of iris code space"
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "from cognitive binary logic to cognitive intelligent agents"
    - "handwritten farsi character recognition using artificial neural network"

Connected through hyperedge HE-3 (induced by source paper "combined haar hilbert and log gabor based iris encoders"):
  with members:
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "exploratory simulation of an intelligent iris verifier distributed system"
    - "from cognitive binary logic to cognitive intelligent agents"
    - "comparing haar hilbert and log gabor based iris encoders on bath iris image database"
    - "exploring new directions in iris recognition"

Connected through hyperedge HE-4 (induced by source paper "the biometric menagerie a fuzzy and inconsistent concept"):
  with members:
    - "combined haar hilbert and log gabor based iris encoders"
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "comparing haar hilbert and log gabor based iris encoders on bath iris image database"
    - "exploring new directions in iris recognition"

Connected through hyperedge HE-5 (induced by source paper "noise influence on the fuzzy linguistic partitioning of iris code space"):
  with members:
    - "combined haar hilbert and log gabor based iris encoders"
    - "8 valent fuzzy logic for iris recognition and biometry"
    - "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.NE(Neural and Evolutionary Computing)